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<h1 id="sec_name">
<span data-if="hdevelop" style="display:inline;">read_dl_model</span><span data-if="c" style="display:none;">T_read_dl_model</span><span data-if="cpp" style="display:none;">ReadDlModel</span><span data-if="dotnet" style="display:none;">ReadDlModel</span><span data-if="python" style="display:none;">read_dl_model</span> (算子名称)</h1>
<h2>名称</h2>
<p><code><span data-if="hdevelop" style="display:inline;">read_dl_model</span><span data-if="c" style="display:none;">T_read_dl_model</span><span data-if="cpp" style="display:none;">ReadDlModel</span><span data-if="dotnet" style="display:none;">ReadDlModel</span><span data-if="python" style="display:none;">read_dl_model</span></code> — Read a deep learning model from a file.</p>
<h2 id="sec_synopsis">参数签名</h2>
<div data-if="hdevelop" style="display:inline;">
<p>
<code><b>read_dl_model</b>( :  : <a href="#FileName"><i>FileName</i></a> : <a href="#DLModelHandle"><i>DLModelHandle</i></a>)</code></p>
</div>
<div data-if="c" style="display:none;">
<p>
<code>Herror <b>T_read_dl_model</b>(const Htuple <a href="#FileName"><i>FileName</i></a>, Htuple* <a href="#DLModelHandle"><i>DLModelHandle</i></a>)</code></p>
</div>
<div data-if="cpp" style="display:none;">
<p>
<code>void <b>ReadDlModel</b>(const HTuple&amp; <a href="#FileName"><i>FileName</i></a>, HTuple* <a href="#DLModelHandle"><i>DLModelHandle</i></a>)</code></p>
<p>
<code>void <a href="HDlModel.html">HDlModel</a>::<b>HDlModel</b>(const HString&amp; <a href="#FileName"><i>FileName</i></a>)</code></p>
<p>
<code>void <a href="HDlModel.html">HDlModel</a>::<b>HDlModel</b>(const char* <a href="#FileName"><i>FileName</i></a>)</code></p>
<p>
<code>void <a href="HDlModel.html">HDlModel</a>::<b>HDlModel</b>(const wchar_t* <a href="#FileName"><i>FileName</i></a>)  <span class="signnote">
            (
            Windows only)
          </span></code></p>
<p>
<code>void <a href="HDlModel.html">HDlModel</a>::<b>ReadDlModel</b>(const HString&amp; <a href="#FileName"><i>FileName</i></a>)</code></p>
<p>
<code>void <a href="HDlModel.html">HDlModel</a>::<b>ReadDlModel</b>(const char* <a href="#FileName"><i>FileName</i></a>)</code></p>
<p>
<code>void <a href="HDlModel.html">HDlModel</a>::<b>ReadDlModel</b>(const wchar_t* <a href="#FileName"><i>FileName</i></a>)  <span class="signnote">
            (
            Windows only)
          </span></code></p>
</div>
<div data-if="com" style="display:none;"></div>
<div data-if="dotnet" style="display:none;">
<p>
<code>static void <a href="HOperatorSet.html">HOperatorSet</a>.<b>ReadDlModel</b>(<a href="HTuple.html">HTuple</a> <a href="#FileName"><i>fileName</i></a>, out <a href="HTuple.html">HTuple</a> <a href="#DLModelHandle"><i>DLModelHandle</i></a>)</code></p>
<p>
<code>public <a href="HDlModel.html">HDlModel</a>(string <a href="#FileName"><i>fileName</i></a>)</code></p>
<p>
<code>void <a href="HDlModel.html">HDlModel</a>.<b>ReadDlModel</b>(string <a href="#FileName"><i>fileName</i></a>)</code></p>
</div>
<div data-if="python" style="display:none;">
<p>
<code>def <b>read_dl_model</b>(<a href="#FileName"><i>file_name</i></a>: str) -&gt; HHandle</code></p>
</div>
<h2 id="sec_description">描述</h2>
<p>该算子 <code><span data-if="hdevelop" style="display:inline">read_dl_model</span><span data-if="c" style="display:none">read_dl_model</span><span data-if="cpp" style="display:none">ReadDlModel</span><span data-if="com" style="display:none">ReadDlModel</span><span data-if="dotnet" style="display:none">ReadDlModel</span><span data-if="python" style="display:none">read_dl_model</span></code> reads a deep learning model.
Such models have to be in the HALCON format or in the ONNX format
(see the reference below). Restrictions apply to the latter.
As a result, the handle <a href="#DLModelHandle"><i><code><span data-if="hdevelop" style="display:inline">DLModelHandle</span><span data-if="c" style="display:none">DLModelHandle</span><span data-if="cpp" style="display:none">DLModelHandle</span><span data-if="com" style="display:none">DLModelHandle</span><span data-if="dotnet" style="display:none">DLModelHandle</span><span data-if="python" style="display:none">dlmodel_handle</span></code></i></a> is returned.
</p>
<p>The model is loaded from the file <a href="#FileName"><i><code><span data-if="hdevelop" style="display:inline">FileName</span><span data-if="c" style="display:none">FileName</span><span data-if="cpp" style="display:none">FileName</span><span data-if="com" style="display:none">FileName</span><span data-if="dotnet" style="display:none">fileName</span><span data-if="python" style="display:none">file_name</span></code></i></a>.
This file is thereby searched in the directory <code>$HALCONROOT/dl/</code>
as well as in the currently used directory.
The default HALCON file extension for deep learning networks is
<i><span data-if="hdevelop" style="display:inline">'.hdl'</span><span data-if="c" style="display:none">".hdl"</span><span data-if="cpp" style="display:none">".hdl"</span><span data-if="com" style="display:none">".hdl"</span><span data-if="dotnet" style="display:none">".hdl"</span><span data-if="python" style="display:none">".hdl"</span></i>.
</p>
<p>Please note that the values of runtime specific parameters are not written
to file, see <a href="write_dl_model.html"><code><span data-if="hdevelop" style="display:inline">write_dl_model</span><span data-if="c" style="display:none">write_dl_model</span><span data-if="cpp" style="display:none">WriteDlModel</span><span data-if="com" style="display:none">WriteDlModel</span><span data-if="dotnet" style="display:none">WriteDlModel</span><span data-if="python" style="display:none">write_dl_model</span></code></a>.
As a consequence, when reading a model, these parameters are initialized
with their default value, see <a href="get_dl_model_param.html"><code><span data-if="hdevelop" style="display:inline">get_dl_model_param</span><span data-if="c" style="display:none">get_dl_model_param</span><span data-if="cpp" style="display:none">GetDlModelParam</span><span data-if="com" style="display:none">GetDlModelParam</span><span data-if="dotnet" style="display:none">GetDlModelParam</span><span data-if="python" style="display:none">get_dl_model_param</span></code></a>.
</p>
<p>For further explanations on deep learning models in HALCON,
see the chapter <a href="toc_deeplearning_model.html">Deep Learning / Model</a>.
</p>
<h3>Reading in a Model Provided by HALCON</h3>
<p>HALCON provides pretrained neural networks for classification and
semantic segmentation. These neural networks are good starting points when
training a custom network. They have been pretrained on a large image dataset.
For anomaly detection, HALCON provides initial models.
</p>
<dl class="generic">

<dt><b>Models for 3D Gripping Point Detection</b></dt>
<dd>
<p>

The following network is provided for 3D Gripping Point Detection:
</p>
<dl class="generic">

<dt><b><i><span data-if="hdevelop" style="display:inline">'pretrained_dl_3d_gripping_point.hdl'</span><span data-if="c" style="display:none">"pretrained_dl_3d_gripping_point.hdl"</span><span data-if="cpp" style="display:none">"pretrained_dl_3d_gripping_point.hdl"</span><span data-if="com" style="display:none">"pretrained_dl_3d_gripping_point.hdl"</span><span data-if="dotnet" style="display:none">"pretrained_dl_3d_gripping_point.hdl"</span><span data-if="python" style="display:none">"pretrained_dl_3d_gripping_point.hdl"</span></i></b></dt>
<dd>
<p>

The network expects up to 5 images of type <code>real</code>:
</p>
<dl class="generic">

<dt><b></b></dt>
<dd><p>
 <i><span data-if="hdevelop" style="display:inline">'image'</span><span data-if="c" style="display:none">"image"</span><span data-if="cpp" style="display:none">"image"</span><span data-if="com" style="display:none">"image"</span><span data-if="dotnet" style="display:none">"image"</span><span data-if="python" style="display:none">"image"</span></i>: intensity (gray value) image
</p></dd>

<dt><b></b></dt>
<dd><p>
 <i><span data-if="hdevelop" style="display:inline">'x'</span><span data-if="c" style="display:none">"x"</span><span data-if="cpp" style="display:none">"x"</span><span data-if="com" style="display:none">"x"</span><span data-if="dotnet" style="display:none">"x"</span><span data-if="python" style="display:none">"x"</span></i>: X-image (values need to increase from left to right)
</p></dd>

<dt><b></b></dt>
<dd><p>
 <i><span data-if="hdevelop" style="display:inline">'y'</span><span data-if="c" style="display:none">"y"</span><span data-if="cpp" style="display:none">"y"</span><span data-if="com" style="display:none">"y"</span><span data-if="dotnet" style="display:none">"y"</span><span data-if="python" style="display:none">"y"</span></i>: Y-image (values need to increase from top to bottom)
</p></dd>

<dt><b></b></dt>
<dd><p>
 <i><span data-if="hdevelop" style="display:inline">'z'</span><span data-if="c" style="display:none">"z"</span><span data-if="cpp" style="display:none">"z"</span><span data-if="com" style="display:none">"z"</span><span data-if="dotnet" style="display:none">"z"</span><span data-if="python" style="display:none">"z"</span></i>: Z-image (values need to increase from points close to the sensor to
far points; this is for example the case if the data is given in the
camera coordinate system)
</p></dd>

<dt><b></b></dt>
<dd><p>
 <i><span data-if="hdevelop" style="display:inline">'normals'</span><span data-if="c" style="display:none">"normals"</span><span data-if="cpp" style="display:none">"normals"</span><span data-if="com" style="display:none">"normals"</span><span data-if="dotnet" style="display:none">"normals"</span><span data-if="python" style="display:none">"normals"</span></i>: 2D mappings
</p></dd>
</dl>
<p>
Additionally, the network requires certain image properties (for all input images
mentioned above). The corresponding values can be retrieved with
<a href="get_dl_model_param.html"><code><span data-if="hdevelop" style="display:inline">get_dl_model_param</span><span data-if="c" style="display:none">get_dl_model_param</span><span data-if="cpp" style="display:none">GetDlModelParam</span><span data-if="com" style="display:none">GetDlModelParam</span><span data-if="dotnet" style="display:none">GetDlModelParam</span><span data-if="python" style="display:none">get_dl_model_param</span></code></a>. Here we list the default values:
</p>
<dl class="generic">

<dt><b></b></dt>
<dd><p>
 <i><span data-if="hdevelop" style="display:inline">'image_width'</span><span data-if="c" style="display:none">"image_width"</span><span data-if="cpp" style="display:none">"image_width"</span><span data-if="com" style="display:none">"image_width"</span><span data-if="dotnet" style="display:none">"image_width"</span><span data-if="python" style="display:none">"image_width"</span></i>: 640
</p></dd>

<dt><b></b></dt>
<dd><p>
 <i><span data-if="hdevelop" style="display:inline">'image_height'</span><span data-if="c" style="display:none">"image_height"</span><span data-if="cpp" style="display:none">"image_height"</span><span data-if="com" style="display:none">"image_height"</span><span data-if="dotnet" style="display:none">"image_height"</span><span data-if="python" style="display:none">"image_height"</span></i>: 480
</p></dd>
</dl>

<p>The network architecture allows changes concerning the image dimensions.
</p>
</dd>
</dl>

</dd>

<dt><b>Models for Anomaly Detection</b></dt>
<dd>
<p>

The following networks are provided for anomaly detection:
</p>
<dl class="generic">

<dt><b><i><span data-if="hdevelop" style="display:inline">'initial_dl_anomaly_medium.hdl'</span><span data-if="c" style="display:none">"initial_dl_anomaly_medium.hdl"</span><span data-if="cpp" style="display:none">"initial_dl_anomaly_medium.hdl"</span><span data-if="com" style="display:none">"initial_dl_anomaly_medium.hdl"</span><span data-if="dotnet" style="display:none">"initial_dl_anomaly_medium.hdl"</span><span data-if="python" style="display:none">"initial_dl_anomaly_medium.hdl"</span></i></b></dt>
<dd>
<p>

This neural network is designed to be memory and runtime efficient.
</p>
<p>The network expects the images to be of the type <code>real</code>.
Additionally, the network requires certain image properties.
The corresponding values can be retrieved with
<a href="get_dl_model_param.html"><code><span data-if="hdevelop" style="display:inline">get_dl_model_param</span><span data-if="c" style="display:none">get_dl_model_param</span><span data-if="cpp" style="display:none">GetDlModelParam</span><span data-if="com" style="display:none">GetDlModelParam</span><span data-if="dotnet" style="display:none">GetDlModelParam</span><span data-if="python" style="display:none">get_dl_model_param</span></code></a>. Here we list the default values:
</p>
<dl class="generic">

<dt><b></b></dt>
<dd><p>
 <i><span data-if="hdevelop" style="display:inline">'image_width'</span><span data-if="c" style="display:none">"image_width"</span><span data-if="cpp" style="display:none">"image_width"</span><span data-if="com" style="display:none">"image_width"</span><span data-if="dotnet" style="display:none">"image_width"</span><span data-if="python" style="display:none">"image_width"</span></i>: 480
</p></dd>

<dt><b></b></dt>
<dd><p>
 <i><span data-if="hdevelop" style="display:inline">'image_height'</span><span data-if="c" style="display:none">"image_height"</span><span data-if="cpp" style="display:none">"image_height"</span><span data-if="com" style="display:none">"image_height"</span><span data-if="dotnet" style="display:none">"image_height"</span><span data-if="python" style="display:none">"image_height"</span></i>: 480
</p></dd>

<dt><b></b></dt>
<dd><p>
 <i><span data-if="hdevelop" style="display:inline">'image_num_channels'</span><span data-if="c" style="display:none">"image_num_channels"</span><span data-if="cpp" style="display:none">"image_num_channels"</span><span data-if="com" style="display:none">"image_num_channels"</span><span data-if="dotnet" style="display:none">"image_num_channels"</span><span data-if="python" style="display:none">"image_num_channels"</span></i>: 3
</p></dd>

<dt><b></b></dt>
<dd><p>
 <i><span data-if="hdevelop" style="display:inline">'image_range_min'</span><span data-if="c" style="display:none">"image_range_min"</span><span data-if="cpp" style="display:none">"image_range_min"</span><span data-if="com" style="display:none">"image_range_min"</span><span data-if="dotnet" style="display:none">"image_range_min"</span><span data-if="python" style="display:none">"image_range_min"</span></i>: -2
</p></dd>

<dt><b></b></dt>
<dd><p>
 <i><span data-if="hdevelop" style="display:inline">'image_range_max'</span><span data-if="c" style="display:none">"image_range_max"</span><span data-if="cpp" style="display:none">"image_range_max"</span><span data-if="com" style="display:none">"image_range_max"</span><span data-if="dotnet" style="display:none">"image_range_max"</span><span data-if="python" style="display:none">"image_range_max"</span></i>: 2
</p></dd>
</dl>

<p>The network architecture allows changes concerning the image dimensions,
but the sizes <i><span data-if="hdevelop" style="display:inline">'image_width'</span><span data-if="c" style="display:none">"image_width"</span><span data-if="cpp" style="display:none">"image_width"</span><span data-if="com" style="display:none">"image_width"</span><span data-if="dotnet" style="display:none">"image_width"</span><span data-if="python" style="display:none">"image_width"</span></i> and <i><span data-if="hdevelop" style="display:inline">'image_height'</span><span data-if="c" style="display:none">"image_height"</span><span data-if="cpp" style="display:none">"image_height"</span><span data-if="com" style="display:none">"image_height"</span><span data-if="dotnet" style="display:none">"image_height"</span><span data-if="python" style="display:none">"image_height"</span></i> have to
be multiples of 32 pixels, resulting in a minimum of 32 pixels.
</p>
</dd>

<dt><b><i><span data-if="hdevelop" style="display:inline">'initial_dl_anomaly_large.hdl'</span><span data-if="c" style="display:none">"initial_dl_anomaly_large.hdl"</span><span data-if="cpp" style="display:none">"initial_dl_anomaly_large.hdl"</span><span data-if="com" style="display:none">"initial_dl_anomaly_large.hdl"</span><span data-if="dotnet" style="display:none">"initial_dl_anomaly_large.hdl"</span><span data-if="python" style="display:none">"initial_dl_anomaly_large.hdl"</span></i></b></dt>
<dd>
<p>

This neural network is assumed to be better suited for more complex
anomaly detection tasks.
This comes at the cost of being more time and memory demanding.
</p>
<p>The network expects the images to be of the type <code>real</code>.
Additionally, the network requires certain image properties.
The corresponding values can be retrieved with
<a href="get_dl_model_param.html"><code><span data-if="hdevelop" style="display:inline">get_dl_model_param</span><span data-if="c" style="display:none">get_dl_model_param</span><span data-if="cpp" style="display:none">GetDlModelParam</span><span data-if="com" style="display:none">GetDlModelParam</span><span data-if="dotnet" style="display:none">GetDlModelParam</span><span data-if="python" style="display:none">get_dl_model_param</span></code></a>. Here we list the default values:
</p>
<dl class="generic">

<dt><b></b></dt>
<dd><p>
 <i><span data-if="hdevelop" style="display:inline">'image_width'</span><span data-if="c" style="display:none">"image_width"</span><span data-if="cpp" style="display:none">"image_width"</span><span data-if="com" style="display:none">"image_width"</span><span data-if="dotnet" style="display:none">"image_width"</span><span data-if="python" style="display:none">"image_width"</span></i>: 480
</p></dd>

<dt><b></b></dt>
<dd><p>
 <i><span data-if="hdevelop" style="display:inline">'image_height'</span><span data-if="c" style="display:none">"image_height"</span><span data-if="cpp" style="display:none">"image_height"</span><span data-if="com" style="display:none">"image_height"</span><span data-if="dotnet" style="display:none">"image_height"</span><span data-if="python" style="display:none">"image_height"</span></i>: 480
</p></dd>

<dt><b></b></dt>
<dd><p>
 <i><span data-if="hdevelop" style="display:inline">'image_num_channels'</span><span data-if="c" style="display:none">"image_num_channels"</span><span data-if="cpp" style="display:none">"image_num_channels"</span><span data-if="com" style="display:none">"image_num_channels"</span><span data-if="dotnet" style="display:none">"image_num_channels"</span><span data-if="python" style="display:none">"image_num_channels"</span></i>: 3
</p></dd>

<dt><b></b></dt>
<dd><p>
 <i><span data-if="hdevelop" style="display:inline">'image_range_min'</span><span data-if="c" style="display:none">"image_range_min"</span><span data-if="cpp" style="display:none">"image_range_min"</span><span data-if="com" style="display:none">"image_range_min"</span><span data-if="dotnet" style="display:none">"image_range_min"</span><span data-if="python" style="display:none">"image_range_min"</span></i>: -2
</p></dd>

<dt><b></b></dt>
<dd><p>
 <i><span data-if="hdevelop" style="display:inline">'image_range_max'</span><span data-if="c" style="display:none">"image_range_max"</span><span data-if="cpp" style="display:none">"image_range_max"</span><span data-if="com" style="display:none">"image_range_max"</span><span data-if="dotnet" style="display:none">"image_range_max"</span><span data-if="python" style="display:none">"image_range_max"</span></i>: 2
</p></dd>
</dl>

<p>The network architecture allows changes concerning the image dimensions,
but  the sizes <i><span data-if="hdevelop" style="display:inline">'image_width'</span><span data-if="c" style="display:none">"image_width"</span><span data-if="cpp" style="display:none">"image_width"</span><span data-if="com" style="display:none">"image_width"</span><span data-if="dotnet" style="display:none">"image_width"</span><span data-if="python" style="display:none">"image_width"</span></i> and <i><span data-if="hdevelop" style="display:inline">'image_height'</span><span data-if="c" style="display:none">"image_height"</span><span data-if="cpp" style="display:none">"image_height"</span><span data-if="com" style="display:none">"image_height"</span><span data-if="dotnet" style="display:none">"image_height"</span><span data-if="python" style="display:none">"image_height"</span></i> have to
be multiples of 32 pixels, resulting in a minimum of 32 pixels.
</p>
</dd>
</dl>

</dd>

<dt><b>Models for Global Context Anomaly Detection</b></dt>
<dd>
<p>

The following networks are provided for Global Context Anomaly Detection:
</p>
<dl class="generic">

<dt><b><i><span data-if="hdevelop" style="display:inline">'pretrained_dl_anomaly_global_context.hdl'</span><span data-if="c" style="display:none">"pretrained_dl_anomaly_global_context.hdl"</span><span data-if="cpp" style="display:none">"pretrained_dl_anomaly_global_context.hdl"</span><span data-if="com" style="display:none">"pretrained_dl_anomaly_global_context.hdl"</span><span data-if="dotnet" style="display:none">"pretrained_dl_anomaly_global_context.hdl"</span><span data-if="python" style="display:none">"pretrained_dl_anomaly_global_context.hdl"</span></i></b></dt>
<dd>
<p>

The network expects the images to be of the type <code>real</code>.
Additionally, the network requires certain image properties.
The corresponding values can be retrieved with
<a href="get_dl_model_param.html"><code><span data-if="hdevelop" style="display:inline">get_dl_model_param</span><span data-if="c" style="display:none">get_dl_model_param</span><span data-if="cpp" style="display:none">GetDlModelParam</span><span data-if="com" style="display:none">GetDlModelParam</span><span data-if="dotnet" style="display:none">GetDlModelParam</span><span data-if="python" style="display:none">get_dl_model_param</span></code></a>. Here we list the default values:
</p>
<dl class="generic">

<dt><b></b></dt>
<dd><p>
 <i><span data-if="hdevelop" style="display:inline">'image_width'</span><span data-if="c" style="display:none">"image_width"</span><span data-if="cpp" style="display:none">"image_width"</span><span data-if="com" style="display:none">"image_width"</span><span data-if="dotnet" style="display:none">"image_width"</span><span data-if="python" style="display:none">"image_width"</span></i>: 256
</p></dd>

<dt><b></b></dt>
<dd><p>
 <i><span data-if="hdevelop" style="display:inline">'image_height'</span><span data-if="c" style="display:none">"image_height"</span><span data-if="cpp" style="display:none">"image_height"</span><span data-if="com" style="display:none">"image_height"</span><span data-if="dotnet" style="display:none">"image_height"</span><span data-if="python" style="display:none">"image_height"</span></i>: 256
</p></dd>

<dt><b></b></dt>
<dd><p>
 <i><span data-if="hdevelop" style="display:inline">'image_num_channels'</span><span data-if="c" style="display:none">"image_num_channels"</span><span data-if="cpp" style="display:none">"image_num_channels"</span><span data-if="com" style="display:none">"image_num_channels"</span><span data-if="dotnet" style="display:none">"image_num_channels"</span><span data-if="python" style="display:none">"image_num_channels"</span></i>: 3
</p></dd>

<dt><b></b></dt>
<dd><p>
 <i><span data-if="hdevelop" style="display:inline">'image_range_min'</span><span data-if="c" style="display:none">"image_range_min"</span><span data-if="cpp" style="display:none">"image_range_min"</span><span data-if="com" style="display:none">"image_range_min"</span><span data-if="dotnet" style="display:none">"image_range_min"</span><span data-if="python" style="display:none">"image_range_min"</span></i>: -127.0
</p></dd>

<dt><b></b></dt>
<dd><p>
 <i><span data-if="hdevelop" style="display:inline">'image_range_max'</span><span data-if="c" style="display:none">"image_range_max"</span><span data-if="cpp" style="display:none">"image_range_max"</span><span data-if="com" style="display:none">"image_range_max"</span><span data-if="dotnet" style="display:none">"image_range_max"</span><span data-if="python" style="display:none">"image_range_max"</span></i>: 128.0
</p></dd>
</dl>
</dd>
</dl>

</dd>

<dt><b>Models for Classification</b></dt>
<dd>
<p>

The following pretrained neural networks are provided for
classification and usable as backbones for detection:
</p>
<dl class="generic">

<dt><b><i><span data-if="hdevelop" style="display:inline">'pretrained_dl_classifier_alexnet.hdl'</span><span data-if="c" style="display:none">"pretrained_dl_classifier_alexnet.hdl"</span><span data-if="cpp" style="display:none">"pretrained_dl_classifier_alexnet.hdl"</span><span data-if="com" style="display:none">"pretrained_dl_classifier_alexnet.hdl"</span><span data-if="dotnet" style="display:none">"pretrained_dl_classifier_alexnet.hdl"</span><span data-if="python" style="display:none">"pretrained_dl_classifier_alexnet.hdl"</span></i>:</b></dt>
<dd>
<p>

This neural network is designed for simple classification tasks.
It is characterized by its convolution kernels in the first
convolution layers, which are larger than those in other networks with
comparable classification performance
(e.g., <i><span data-if="hdevelop" style="display:inline">'pretrained_dl_classifier_compact.hdl'</span><span data-if="c" style="display:none">"pretrained_dl_classifier_compact.hdl"</span><span data-if="cpp" style="display:none">"pretrained_dl_classifier_compact.hdl"</span><span data-if="com" style="display:none">"pretrained_dl_classifier_compact.hdl"</span><span data-if="dotnet" style="display:none">"pretrained_dl_classifier_compact.hdl"</span><span data-if="python" style="display:none">"pretrained_dl_classifier_compact.hdl"</span></i>).
This may be beneficial for feature extraction.
</p>
<p>This classifier expects the images to be of the type <code>real</code>.
Additionally, the network is designed for certain image properties.
The corresponding values can be retrieved with
<a href="get_dl_model_param.html"><code><span data-if="hdevelop" style="display:inline">get_dl_model_param</span><span data-if="c" style="display:none">get_dl_model_param</span><span data-if="cpp" style="display:none">GetDlModelParam</span><span data-if="com" style="display:none">GetDlModelParam</span><span data-if="dotnet" style="display:none">GetDlModelParam</span><span data-if="python" style="display:none">get_dl_model_param</span></code></a>. Here we list the default values with
which the classifier has been trained:
</p>
<dl class="generic">

<dt><b></b></dt>
<dd><p>
 <i><span data-if="hdevelop" style="display:inline">'image_width'</span><span data-if="c" style="display:none">"image_width"</span><span data-if="cpp" style="display:none">"image_width"</span><span data-if="com" style="display:none">"image_width"</span><span data-if="dotnet" style="display:none">"image_width"</span><span data-if="python" style="display:none">"image_width"</span></i>: 224
</p></dd>

<dt><b></b></dt>
<dd><p>
 <i><span data-if="hdevelop" style="display:inline">'image_height'</span><span data-if="c" style="display:none">"image_height"</span><span data-if="cpp" style="display:none">"image_height"</span><span data-if="com" style="display:none">"image_height"</span><span data-if="dotnet" style="display:none">"image_height"</span><span data-if="python" style="display:none">"image_height"</span></i>: 224
</p></dd>

<dt><b></b></dt>
<dd><p>
 <i><span data-if="hdevelop" style="display:inline">'image_num_channels'</span><span data-if="c" style="display:none">"image_num_channels"</span><span data-if="cpp" style="display:none">"image_num_channels"</span><span data-if="com" style="display:none">"image_num_channels"</span><span data-if="dotnet" style="display:none">"image_num_channels"</span><span data-if="python" style="display:none">"image_num_channels"</span></i>: 3
</p></dd>

<dt><b></b></dt>
<dd><p>
 <i><span data-if="hdevelop" style="display:inline">'image_range_min'</span><span data-if="c" style="display:none">"image_range_min"</span><span data-if="cpp" style="display:none">"image_range_min"</span><span data-if="com" style="display:none">"image_range_min"</span><span data-if="dotnet" style="display:none">"image_range_min"</span><span data-if="python" style="display:none">"image_range_min"</span></i>: -127.0
</p></dd>

<dt><b></b></dt>
<dd><p>
 <i><span data-if="hdevelop" style="display:inline">'image_range_max'</span><span data-if="c" style="display:none">"image_range_max"</span><span data-if="cpp" style="display:none">"image_range_max"</span><span data-if="com" style="display:none">"image_range_max"</span><span data-if="dotnet" style="display:none">"image_range_max"</span><span data-if="python" style="display:none">"image_range_max"</span></i>: 128.0
</p></dd>
</dl>

<p>The network architecture allows changes concerning the image
dimensions. <i><span data-if="hdevelop" style="display:inline">'image_width'</span><span data-if="c" style="display:none">"image_width"</span><span data-if="cpp" style="display:none">"image_width"</span><span data-if="com" style="display:none">"image_width"</span><span data-if="dotnet" style="display:none">"image_width"</span><span data-if="python" style="display:none">"image_width"</span></i> and <i><span data-if="hdevelop" style="display:inline">'image_height'</span><span data-if="c" style="display:none">"image_height"</span><span data-if="cpp" style="display:none">"image_height"</span><span data-if="com" style="display:none">"image_height"</span><span data-if="dotnet" style="display:none">"image_height"</span><span data-if="python" style="display:none">"image_height"</span></i>
should not be less than 29 pixels.
There is no maximum image size, but large image sizes will increase
the memory demand and the runtime significantly.
Changing the image size will reinitialize the weights of the fully
connected layers and therefore makes a retraining necessary.
</p>
<p>Note that one can improve the runtime for this network
by fusing the convolution and ReLU layers, see
<a href="set_dl_model_param.html"><code><span data-if="hdevelop" style="display:inline">set_dl_model_param</span><span data-if="c" style="display:none">set_dl_model_param</span><span data-if="cpp" style="display:none">SetDlModelParam</span><span data-if="com" style="display:none">SetDlModelParam</span><span data-if="dotnet" style="display:none">SetDlModelParam</span><span data-if="python" style="display:none">set_dl_model_param</span></code></a> and the parameter
<i><span data-if="hdevelop" style="display:inline">'fuse_conv_relu'</span><span data-if="c" style="display:none">"fuse_conv_relu"</span><span data-if="cpp" style="display:none">"fuse_conv_relu"</span><span data-if="com" style="display:none">"fuse_conv_relu"</span><span data-if="dotnet" style="display:none">"fuse_conv_relu"</span><span data-if="python" style="display:none">"fuse_conv_relu"</span></i>.
</p>
</dd>

<dt><b><i><span data-if="hdevelop" style="display:inline">'pretrained_dl_classifier_compact.hdl'</span><span data-if="c" style="display:none">"pretrained_dl_classifier_compact.hdl"</span><span data-if="cpp" style="display:none">"pretrained_dl_classifier_compact.hdl"</span><span data-if="com" style="display:none">"pretrained_dl_classifier_compact.hdl"</span><span data-if="dotnet" style="display:none">"pretrained_dl_classifier_compact.hdl"</span><span data-if="python" style="display:none">"pretrained_dl_classifier_compact.hdl"</span></i>:</b></dt>
<dd>
<p>

This neural network is designed to be more memory and runtime
efficient.
</p>
<p>The classifier expects the images to be of the type <code>real</code>.
Additionally, the network requires certain image properties.
The corresponding values can be retrieved with
<a href="get_dl_model_param.html"><code><span data-if="hdevelop" style="display:inline">get_dl_model_param</span><span data-if="c" style="display:none">get_dl_model_param</span><span data-if="cpp" style="display:none">GetDlModelParam</span><span data-if="com" style="display:none">GetDlModelParam</span><span data-if="dotnet" style="display:none">GetDlModelParam</span><span data-if="python" style="display:none">get_dl_model_param</span></code></a>. Here we list the default values with
which the classifier has been trained:
</p>
<dl class="generic">

<dt><b></b></dt>
<dd><p>
 <i><span data-if="hdevelop" style="display:inline">'image_width'</span><span data-if="c" style="display:none">"image_width"</span><span data-if="cpp" style="display:none">"image_width"</span><span data-if="com" style="display:none">"image_width"</span><span data-if="dotnet" style="display:none">"image_width"</span><span data-if="python" style="display:none">"image_width"</span></i>: 224
</p></dd>

<dt><b></b></dt>
<dd><p>
 <i><span data-if="hdevelop" style="display:inline">'image_height'</span><span data-if="c" style="display:none">"image_height"</span><span data-if="cpp" style="display:none">"image_height"</span><span data-if="com" style="display:none">"image_height"</span><span data-if="dotnet" style="display:none">"image_height"</span><span data-if="python" style="display:none">"image_height"</span></i>: 224
</p></dd>

<dt><b></b></dt>
<dd><p>
 <i><span data-if="hdevelop" style="display:inline">'image_num_channels'</span><span data-if="c" style="display:none">"image_num_channels"</span><span data-if="cpp" style="display:none">"image_num_channels"</span><span data-if="com" style="display:none">"image_num_channels"</span><span data-if="dotnet" style="display:none">"image_num_channels"</span><span data-if="python" style="display:none">"image_num_channels"</span></i>: 3
</p></dd>

<dt><b></b></dt>
<dd><p>
 <i><span data-if="hdevelop" style="display:inline">'image_range_min'</span><span data-if="c" style="display:none">"image_range_min"</span><span data-if="cpp" style="display:none">"image_range_min"</span><span data-if="com" style="display:none">"image_range_min"</span><span data-if="dotnet" style="display:none">"image_range_min"</span><span data-if="python" style="display:none">"image_range_min"</span></i>: -127.0
</p></dd>

<dt><b></b></dt>
<dd><p>
 <i><span data-if="hdevelop" style="display:inline">'image_range_max'</span><span data-if="c" style="display:none">"image_range_max"</span><span data-if="cpp" style="display:none">"image_range_max"</span><span data-if="com" style="display:none">"image_range_max"</span><span data-if="dotnet" style="display:none">"image_range_max"</span><span data-if="python" style="display:none">"image_range_max"</span></i>: 128.0
</p></dd>
</dl>

<p>This network does not contain any fully connected layer.
The network architecture allows changes concerning the image
dimensions. <i><span data-if="hdevelop" style="display:inline">'image_width'</span><span data-if="c" style="display:none">"image_width"</span><span data-if="cpp" style="display:none">"image_width"</span><span data-if="com" style="display:none">"image_width"</span><span data-if="dotnet" style="display:none">"image_width"</span><span data-if="python" style="display:none">"image_width"</span></i> and <i><span data-if="hdevelop" style="display:inline">'image_height'</span><span data-if="c" style="display:none">"image_height"</span><span data-if="cpp" style="display:none">"image_height"</span><span data-if="com" style="display:none">"image_height"</span><span data-if="dotnet" style="display:none">"image_height"</span><span data-if="python" style="display:none">"image_height"</span></i>
should not be less than 15 pixels.
</p>
</dd>

<dt><b><i><span data-if="hdevelop" style="display:inline">'pretrained_dl_classifier_enhanced.hdl'</span><span data-if="c" style="display:none">"pretrained_dl_classifier_enhanced.hdl"</span><span data-if="cpp" style="display:none">"pretrained_dl_classifier_enhanced.hdl"</span><span data-if="com" style="display:none">"pretrained_dl_classifier_enhanced.hdl"</span><span data-if="dotnet" style="display:none">"pretrained_dl_classifier_enhanced.hdl"</span><span data-if="python" style="display:none">"pretrained_dl_classifier_enhanced.hdl"</span></i>:</b></dt>
<dd>
<p>

This neural network has more hidden layers than
<i><span data-if="hdevelop" style="display:inline">'pretrained_dl_classifier_compact.hdl'</span><span data-if="c" style="display:none">"pretrained_dl_classifier_compact.hdl"</span><span data-if="cpp" style="display:none">"pretrained_dl_classifier_compact.hdl"</span><span data-if="com" style="display:none">"pretrained_dl_classifier_compact.hdl"</span><span data-if="dotnet" style="display:none">"pretrained_dl_classifier_compact.hdl"</span><span data-if="python" style="display:none">"pretrained_dl_classifier_compact.hdl"</span></i> and is therefore
assumed to be better suited for more complex classification tasks.
This comes at the cost of being more time and memory demanding.
</p>
<p>The classifier expects the images to be of the type <code>real</code>.
Additionally, the network requires certain image properties.
The corresponding values can be retrieved with
<a href="get_dl_model_param.html"><code><span data-if="hdevelop" style="display:inline">get_dl_model_param</span><span data-if="c" style="display:none">get_dl_model_param</span><span data-if="cpp" style="display:none">GetDlModelParam</span><span data-if="com" style="display:none">GetDlModelParam</span><span data-if="dotnet" style="display:none">GetDlModelParam</span><span data-if="python" style="display:none">get_dl_model_param</span></code></a>. Here we list the default values with
which the classifier has been trained:
</p>
<dl class="generic">

<dt><b></b></dt>
<dd><p>
 <i><span data-if="hdevelop" style="display:inline">'image_width'</span><span data-if="c" style="display:none">"image_width"</span><span data-if="cpp" style="display:none">"image_width"</span><span data-if="com" style="display:none">"image_width"</span><span data-if="dotnet" style="display:none">"image_width"</span><span data-if="python" style="display:none">"image_width"</span></i>: 224
</p></dd>

<dt><b></b></dt>
<dd><p>
 <i><span data-if="hdevelop" style="display:inline">'image_height'</span><span data-if="c" style="display:none">"image_height"</span><span data-if="cpp" style="display:none">"image_height"</span><span data-if="com" style="display:none">"image_height"</span><span data-if="dotnet" style="display:none">"image_height"</span><span data-if="python" style="display:none">"image_height"</span></i>: 224
</p></dd>

<dt><b></b></dt>
<dd><p>
 <i><span data-if="hdevelop" style="display:inline">'image_num_channels'</span><span data-if="c" style="display:none">"image_num_channels"</span><span data-if="cpp" style="display:none">"image_num_channels"</span><span data-if="com" style="display:none">"image_num_channels"</span><span data-if="dotnet" style="display:none">"image_num_channels"</span><span data-if="python" style="display:none">"image_num_channels"</span></i>: 3
</p></dd>

<dt><b></b></dt>
<dd><p>
 <i><span data-if="hdevelop" style="display:inline">'image_range_min'</span><span data-if="c" style="display:none">"image_range_min"</span><span data-if="cpp" style="display:none">"image_range_min"</span><span data-if="com" style="display:none">"image_range_min"</span><span data-if="dotnet" style="display:none">"image_range_min"</span><span data-if="python" style="display:none">"image_range_min"</span></i>: -127.0
</p></dd>

<dt><b></b></dt>
<dd><p>
 <i><span data-if="hdevelop" style="display:inline">'image_range_max'</span><span data-if="c" style="display:none">"image_range_max"</span><span data-if="cpp" style="display:none">"image_range_max"</span><span data-if="com" style="display:none">"image_range_max"</span><span data-if="dotnet" style="display:none">"image_range_max"</span><span data-if="python" style="display:none">"image_range_max"</span></i>: 128.0
</p></dd>
</dl>

<p>The network architecture allows changes concerning the image
dimensions. <i><span data-if="hdevelop" style="display:inline">'image_width'</span><span data-if="c" style="display:none">"image_width"</span><span data-if="cpp" style="display:none">"image_width"</span><span data-if="com" style="display:none">"image_width"</span><span data-if="dotnet" style="display:none">"image_width"</span><span data-if="python" style="display:none">"image_width"</span></i> and <i><span data-if="hdevelop" style="display:inline">'image_height'</span><span data-if="c" style="display:none">"image_height"</span><span data-if="cpp" style="display:none">"image_height"</span><span data-if="com" style="display:none">"image_height"</span><span data-if="dotnet" style="display:none">"image_height"</span><span data-if="python" style="display:none">"image_height"</span></i>
should not be less than 47 pixels.
There is no maximum image size, but large image sizes will increase
the memory demand and the runtime significantly.
Changing the image size will reinitialize the weights of the fully
connected layers and therefore makes a retraining necessary.
</p>
</dd>

<dt><b><i><span data-if="hdevelop" style="display:inline">'pretrained_dl_classifier_mobilenet_v2.hdl'</span><span data-if="c" style="display:none">"pretrained_dl_classifier_mobilenet_v2.hdl"</span><span data-if="cpp" style="display:none">"pretrained_dl_classifier_mobilenet_v2.hdl"</span><span data-if="com" style="display:none">"pretrained_dl_classifier_mobilenet_v2.hdl"</span><span data-if="dotnet" style="display:none">"pretrained_dl_classifier_mobilenet_v2.hdl"</span><span data-if="python" style="display:none">"pretrained_dl_classifier_mobilenet_v2.hdl"</span></i>:</b></dt>
<dd>
<p>

This classifier is a small and low-power model, for what reason it is more
suitable for mobile and embedded vision applications.
</p>
<p>The classifier expects the images to be of the type <code>real</code>.
Additionally, the network requires certain image properties.
The corresponding values can be retrieved with
<a href="get_dl_model_param.html"><code><span data-if="hdevelop" style="display:inline">get_dl_model_param</span><span data-if="c" style="display:none">get_dl_model_param</span><span data-if="cpp" style="display:none">GetDlModelParam</span><span data-if="com" style="display:none">GetDlModelParam</span><span data-if="dotnet" style="display:none">GetDlModelParam</span><span data-if="python" style="display:none">get_dl_model_param</span></code></a>. Here we list the default values with
which the classifier has been trained:
</p>
<dl class="generic">

<dt><b></b></dt>
<dd><p>
 <i><span data-if="hdevelop" style="display:inline">'image_width'</span><span data-if="c" style="display:none">"image_width"</span><span data-if="cpp" style="display:none">"image_width"</span><span data-if="com" style="display:none">"image_width"</span><span data-if="dotnet" style="display:none">"image_width"</span><span data-if="python" style="display:none">"image_width"</span></i>: 224
</p></dd>

<dt><b></b></dt>
<dd><p>
 <i><span data-if="hdevelop" style="display:inline">'image_height'</span><span data-if="c" style="display:none">"image_height"</span><span data-if="cpp" style="display:none">"image_height"</span><span data-if="com" style="display:none">"image_height"</span><span data-if="dotnet" style="display:none">"image_height"</span><span data-if="python" style="display:none">"image_height"</span></i>: 224
</p></dd>

<dt><b></b></dt>
<dd><p>
 <i><span data-if="hdevelop" style="display:inline">'image_num_channels'</span><span data-if="c" style="display:none">"image_num_channels"</span><span data-if="cpp" style="display:none">"image_num_channels"</span><span data-if="com" style="display:none">"image_num_channels"</span><span data-if="dotnet" style="display:none">"image_num_channels"</span><span data-if="python" style="display:none">"image_num_channels"</span></i>: 3
</p></dd>

<dt><b></b></dt>
<dd><p>
 <i><span data-if="hdevelop" style="display:inline">'image_range_min'</span><span data-if="c" style="display:none">"image_range_min"</span><span data-if="cpp" style="display:none">"image_range_min"</span><span data-if="com" style="display:none">"image_range_min"</span><span data-if="dotnet" style="display:none">"image_range_min"</span><span data-if="python" style="display:none">"image_range_min"</span></i>: -127.0
</p></dd>

<dt><b></b></dt>
<dd><p>
 <i><span data-if="hdevelop" style="display:inline">'image_range_max'</span><span data-if="c" style="display:none">"image_range_max"</span><span data-if="cpp" style="display:none">"image_range_max"</span><span data-if="com" style="display:none">"image_range_max"</span><span data-if="dotnet" style="display:none">"image_range_max"</span><span data-if="python" style="display:none">"image_range_max"</span></i>: 128.0
</p></dd>
</dl>

<p>The network architecture allows changes concerning the image
dimensions. <i><span data-if="hdevelop" style="display:inline">'image_width'</span><span data-if="c" style="display:none">"image_width"</span><span data-if="cpp" style="display:none">"image_width"</span><span data-if="com" style="display:none">"image_width"</span><span data-if="dotnet" style="display:none">"image_width"</span><span data-if="python" style="display:none">"image_width"</span></i> and <i><span data-if="hdevelop" style="display:inline">'image_height'</span><span data-if="c" style="display:none">"image_height"</span><span data-if="cpp" style="display:none">"image_height"</span><span data-if="com" style="display:none">"image_height"</span><span data-if="dotnet" style="display:none">"image_height"</span><span data-if="python" style="display:none">"image_height"</span></i>
should not be less than 32 pixels.
There is no maximum image size, but large image sizes will increase
the memory demand and the runtime significantly.
</p>
<p>On the GPU, the network architecture can benefit greatly from special
optimizations, without which the network can be significantly slower.
</p>
</dd>

<dt><b><i><span data-if="hdevelop" style="display:inline">'pretrained_dl_classifier_resnet18.hdl'</span><span data-if="c" style="display:none">"pretrained_dl_classifier_resnet18.hdl"</span><span data-if="cpp" style="display:none">"pretrained_dl_classifier_resnet18.hdl"</span><span data-if="com" style="display:none">"pretrained_dl_classifier_resnet18.hdl"</span><span data-if="dotnet" style="display:none">"pretrained_dl_classifier_resnet18.hdl"</span><span data-if="python" style="display:none">"pretrained_dl_classifier_resnet18.hdl"</span></i>:</b></dt>
<dd>
<p>

As the neural network
<i><span data-if="hdevelop" style="display:inline">'pretrained_dl_classifier_enhanced.hdl'</span><span data-if="c" style="display:none">"pretrained_dl_classifier_enhanced.hdl"</span><span data-if="cpp" style="display:none">"pretrained_dl_classifier_enhanced.hdl"</span><span data-if="com" style="display:none">"pretrained_dl_classifier_enhanced.hdl"</span><span data-if="dotnet" style="display:none">"pretrained_dl_classifier_enhanced.hdl"</span><span data-if="python" style="display:none">"pretrained_dl_classifier_enhanced.hdl"</span></i>, this classifier is
suited for more complex tasks.
However, due to its special structure, it provides the advantage of making
the training more stable and internally more robust. Compared to
the neural network <i><span data-if="hdevelop" style="display:inline">'pretrained_dl_classifier_resnet50.hdl'</span><span data-if="c" style="display:none">"pretrained_dl_classifier_resnet50.hdl"</span><span data-if="cpp" style="display:none">"pretrained_dl_classifier_resnet50.hdl"</span><span data-if="com" style="display:none">"pretrained_dl_classifier_resnet50.hdl"</span><span data-if="dotnet" style="display:none">"pretrained_dl_classifier_resnet50.hdl"</span><span data-if="python" style="display:none">"pretrained_dl_classifier_resnet50.hdl"</span></i>
it is less complex and has faster inference times.
</p>
<p>The classifier expects the images to be of the type <code>real</code>.
Additionally, the network requires certain image properties.
The corresponding values can be retrieved with
<a href="get_dl_model_param.html"><code><span data-if="hdevelop" style="display:inline">get_dl_model_param</span><span data-if="c" style="display:none">get_dl_model_param</span><span data-if="cpp" style="display:none">GetDlModelParam</span><span data-if="com" style="display:none">GetDlModelParam</span><span data-if="dotnet" style="display:none">GetDlModelParam</span><span data-if="python" style="display:none">get_dl_model_param</span></code></a>. Here we list the default values with
which the classifier has been trained:
</p>
<dl class="generic">

<dt><b></b></dt>
<dd><p>
 <i><span data-if="hdevelop" style="display:inline">'image_width'</span><span data-if="c" style="display:none">"image_width"</span><span data-if="cpp" style="display:none">"image_width"</span><span data-if="com" style="display:none">"image_width"</span><span data-if="dotnet" style="display:none">"image_width"</span><span data-if="python" style="display:none">"image_width"</span></i>: 224
</p></dd>

<dt><b></b></dt>
<dd><p>
 <i><span data-if="hdevelop" style="display:inline">'image_height'</span><span data-if="c" style="display:none">"image_height"</span><span data-if="cpp" style="display:none">"image_height"</span><span data-if="com" style="display:none">"image_height"</span><span data-if="dotnet" style="display:none">"image_height"</span><span data-if="python" style="display:none">"image_height"</span></i>: 224
</p></dd>

<dt><b></b></dt>
<dd><p>
 <i><span data-if="hdevelop" style="display:inline">'image_num_channels'</span><span data-if="c" style="display:none">"image_num_channels"</span><span data-if="cpp" style="display:none">"image_num_channels"</span><span data-if="com" style="display:none">"image_num_channels"</span><span data-if="dotnet" style="display:none">"image_num_channels"</span><span data-if="python" style="display:none">"image_num_channels"</span></i>: 3
</p></dd>

<dt><b></b></dt>
<dd><p>
 <i><span data-if="hdevelop" style="display:inline">'image_range_min'</span><span data-if="c" style="display:none">"image_range_min"</span><span data-if="cpp" style="display:none">"image_range_min"</span><span data-if="com" style="display:none">"image_range_min"</span><span data-if="dotnet" style="display:none">"image_range_min"</span><span data-if="python" style="display:none">"image_range_min"</span></i>: -127.0
</p></dd>

<dt><b></b></dt>
<dd><p>
 <i><span data-if="hdevelop" style="display:inline">'image_range_max'</span><span data-if="c" style="display:none">"image_range_max"</span><span data-if="cpp" style="display:none">"image_range_max"</span><span data-if="com" style="display:none">"image_range_max"</span><span data-if="dotnet" style="display:none">"image_range_max"</span><span data-if="python" style="display:none">"image_range_max"</span></i>: 128.0
</p></dd>
</dl>

<p>The network architecture allows changes concerning the image
dimensions. <i><span data-if="hdevelop" style="display:inline">'image_width'</span><span data-if="c" style="display:none">"image_width"</span><span data-if="cpp" style="display:none">"image_width"</span><span data-if="com" style="display:none">"image_width"</span><span data-if="dotnet" style="display:none">"image_width"</span><span data-if="python" style="display:none">"image_width"</span></i> and <i><span data-if="hdevelop" style="display:inline">'image_height'</span><span data-if="c" style="display:none">"image_height"</span><span data-if="cpp" style="display:none">"image_height"</span><span data-if="com" style="display:none">"image_height"</span><span data-if="dotnet" style="display:none">"image_height"</span><span data-if="python" style="display:none">"image_height"</span></i>
should not be less than 32 pixels.
There is no maximum image size, but large image sizes will increase
the memory demand and the runtime significantly.
Despite the fully connected layer a change of the image size does
not lead to a reinitialization of the weights.
</p>
</dd>

<dt><b><i><span data-if="hdevelop" style="display:inline">'pretrained_dl_classifier_resnet50.hdl'</span><span data-if="c" style="display:none">"pretrained_dl_classifier_resnet50.hdl"</span><span data-if="cpp" style="display:none">"pretrained_dl_classifier_resnet50.hdl"</span><span data-if="com" style="display:none">"pretrained_dl_classifier_resnet50.hdl"</span><span data-if="dotnet" style="display:none">"pretrained_dl_classifier_resnet50.hdl"</span><span data-if="python" style="display:none">"pretrained_dl_classifier_resnet50.hdl"</span></i>:</b></dt>
<dd>
<p>

As the neural network
<i><span data-if="hdevelop" style="display:inline">'pretrained_dl_classifier_enhanced.hdl'</span><span data-if="c" style="display:none">"pretrained_dl_classifier_enhanced.hdl"</span><span data-if="cpp" style="display:none">"pretrained_dl_classifier_enhanced.hdl"</span><span data-if="com" style="display:none">"pretrained_dl_classifier_enhanced.hdl"</span><span data-if="dotnet" style="display:none">"pretrained_dl_classifier_enhanced.hdl"</span><span data-if="python" style="display:none">"pretrained_dl_classifier_enhanced.hdl"</span></i>, this classifier is
suited for more complex tasks.
However, due to its special structure, it provides the advantage of making
the training more stable and internally more robust.
</p>
<p>The classifier expects the images to be of the type <code>real</code>.
Additionally, the network requires certain image properties.
The corresponding values can be retrieved with
<a href="get_dl_model_param.html"><code><span data-if="hdevelop" style="display:inline">get_dl_model_param</span><span data-if="c" style="display:none">get_dl_model_param</span><span data-if="cpp" style="display:none">GetDlModelParam</span><span data-if="com" style="display:none">GetDlModelParam</span><span data-if="dotnet" style="display:none">GetDlModelParam</span><span data-if="python" style="display:none">get_dl_model_param</span></code></a>. Here we list the default values with
which the classifier has been trained:
</p>
<dl class="generic">

<dt><b></b></dt>
<dd><p>
 <i><span data-if="hdevelop" style="display:inline">'image_width'</span><span data-if="c" style="display:none">"image_width"</span><span data-if="cpp" style="display:none">"image_width"</span><span data-if="com" style="display:none">"image_width"</span><span data-if="dotnet" style="display:none">"image_width"</span><span data-if="python" style="display:none">"image_width"</span></i>: 224
</p></dd>

<dt><b></b></dt>
<dd><p>
 <i><span data-if="hdevelop" style="display:inline">'image_height'</span><span data-if="c" style="display:none">"image_height"</span><span data-if="cpp" style="display:none">"image_height"</span><span data-if="com" style="display:none">"image_height"</span><span data-if="dotnet" style="display:none">"image_height"</span><span data-if="python" style="display:none">"image_height"</span></i>: 224
</p></dd>

<dt><b></b></dt>
<dd><p>
 <i><span data-if="hdevelop" style="display:inline">'image_num_channels'</span><span data-if="c" style="display:none">"image_num_channels"</span><span data-if="cpp" style="display:none">"image_num_channels"</span><span data-if="com" style="display:none">"image_num_channels"</span><span data-if="dotnet" style="display:none">"image_num_channels"</span><span data-if="python" style="display:none">"image_num_channels"</span></i>: 3
</p></dd>

<dt><b></b></dt>
<dd><p>
 <i><span data-if="hdevelop" style="display:inline">'image_range_min'</span><span data-if="c" style="display:none">"image_range_min"</span><span data-if="cpp" style="display:none">"image_range_min"</span><span data-if="com" style="display:none">"image_range_min"</span><span data-if="dotnet" style="display:none">"image_range_min"</span><span data-if="python" style="display:none">"image_range_min"</span></i>: -127.0
</p></dd>

<dt><b></b></dt>
<dd><p>
 <i><span data-if="hdevelop" style="display:inline">'image_range_max'</span><span data-if="c" style="display:none">"image_range_max"</span><span data-if="cpp" style="display:none">"image_range_max"</span><span data-if="com" style="display:none">"image_range_max"</span><span data-if="dotnet" style="display:none">"image_range_max"</span><span data-if="python" style="display:none">"image_range_max"</span></i>: 128.0
</p></dd>
</dl>

<p>The network architecture allows changes concerning the image
dimensions. <i><span data-if="hdevelop" style="display:inline">'image_width'</span><span data-if="c" style="display:none">"image_width"</span><span data-if="cpp" style="display:none">"image_width"</span><span data-if="com" style="display:none">"image_width"</span><span data-if="dotnet" style="display:none">"image_width"</span><span data-if="python" style="display:none">"image_width"</span></i> and <i><span data-if="hdevelop" style="display:inline">'image_height'</span><span data-if="c" style="display:none">"image_height"</span><span data-if="cpp" style="display:none">"image_height"</span><span data-if="com" style="display:none">"image_height"</span><span data-if="dotnet" style="display:none">"image_height"</span><span data-if="python" style="display:none">"image_height"</span></i>
should not be less than 32 pixels.
There is no maximum image size, but large image sizes will increase
the memory demand and the runtime significantly.
Despite the fully connected layer a change of the image size does
not lead to a reinitialization of the weights.
</p>
</dd>
</dl>

</dd>

<dt><b>Models for Semantic Segmentation</b></dt>
<dd>
<p>

The following pretrained neural networks are provided for semantic
segmentation:
</p>
<dl class="generic">

<dt><b><i><span data-if="hdevelop" style="display:inline">'pretrained_dl_edge_extractor.hdl'</span><span data-if="c" style="display:none">"pretrained_dl_edge_extractor.hdl"</span><span data-if="cpp" style="display:none">"pretrained_dl_edge_extractor.hdl"</span><span data-if="com" style="display:none">"pretrained_dl_edge_extractor.hdl"</span><span data-if="dotnet" style="display:none">"pretrained_dl_edge_extractor.hdl"</span><span data-if="python" style="display:none">"pretrained_dl_edge_extractor.hdl"</span></i>:</b></dt>
<dd>
<p>

This neural network is designed and pretrained for edge extraction.
As a consequence this model is meant for two class problems
with one class for edges and one for background.
</p>
<p>This network expects the images to be of the type <code>real</code>.
Additionally, the network is designed for certain image properties.
The corresponding values can be retrieved with
<a href="get_dl_model_param.html"><code><span data-if="hdevelop" style="display:inline">get_dl_model_param</span><span data-if="c" style="display:none">get_dl_model_param</span><span data-if="cpp" style="display:none">GetDlModelParam</span><span data-if="com" style="display:none">GetDlModelParam</span><span data-if="dotnet" style="display:none">GetDlModelParam</span><span data-if="python" style="display:none">get_dl_model_param</span></code></a>. Here we list the default values with
which the model has been trained:
</p>
<dl class="generic">

<dt><b></b></dt>
<dd><p>
 <i><span data-if="hdevelop" style="display:inline">'image_width'</span><span data-if="c" style="display:none">"image_width"</span><span data-if="cpp" style="display:none">"image_width"</span><span data-if="com" style="display:none">"image_width"</span><span data-if="dotnet" style="display:none">"image_width"</span><span data-if="python" style="display:none">"image_width"</span></i>: 512
</p></dd>

<dt><b></b></dt>
<dd><p>
 <i><span data-if="hdevelop" style="display:inline">'image_height'</span><span data-if="c" style="display:none">"image_height"</span><span data-if="cpp" style="display:none">"image_height"</span><span data-if="com" style="display:none">"image_height"</span><span data-if="dotnet" style="display:none">"image_height"</span><span data-if="python" style="display:none">"image_height"</span></i>: 512
</p></dd>

<dt><b></b></dt>
<dd><p>
 <i><span data-if="hdevelop" style="display:inline">'image_num_channels'</span><span data-if="c" style="display:none">"image_num_channels"</span><span data-if="cpp" style="display:none">"image_num_channels"</span><span data-if="com" style="display:none">"image_num_channels"</span><span data-if="dotnet" style="display:none">"image_num_channels"</span><span data-if="python" style="display:none">"image_num_channels"</span></i>: 1
</p></dd>

<dt><b></b></dt>
<dd><p>
 <i><span data-if="hdevelop" style="display:inline">'image_range_min'</span><span data-if="c" style="display:none">"image_range_min"</span><span data-if="cpp" style="display:none">"image_range_min"</span><span data-if="com" style="display:none">"image_range_min"</span><span data-if="dotnet" style="display:none">"image_range_min"</span><span data-if="python" style="display:none">"image_range_min"</span></i>: -127.0
</p></dd>

<dt><b></b></dt>
<dd><p>
 <i><span data-if="hdevelop" style="display:inline">'image_range_max'</span><span data-if="c" style="display:none">"image_range_max"</span><span data-if="cpp" style="display:none">"image_range_max"</span><span data-if="com" style="display:none">"image_range_max"</span><span data-if="dotnet" style="display:none">"image_range_max"</span><span data-if="python" style="display:none">"image_range_max"</span></i>: 128.0
</p></dd>

<dt><b></b></dt>
<dd><p>
 <i><span data-if="hdevelop" style="display:inline">'num_classes'</span><span data-if="c" style="display:none">"num_classes"</span><span data-if="cpp" style="display:none">"num_classes"</span><span data-if="com" style="display:none">"num_classes"</span><span data-if="dotnet" style="display:none">"num_classes"</span><span data-if="python" style="display:none">"num_classes"</span></i>: 2
</p></dd>
</dl>

<p>The network architecture allows changes concerning the image dimensions,
but the sizes <i><span data-if="hdevelop" style="display:inline">'image_width'</span><span data-if="c" style="display:none">"image_width"</span><span data-if="cpp" style="display:none">"image_width"</span><span data-if="com" style="display:none">"image_width"</span><span data-if="dotnet" style="display:none">"image_width"</span><span data-if="python" style="display:none">"image_width"</span></i> and <i><span data-if="hdevelop" style="display:inline">'image_height'</span><span data-if="c" style="display:none">"image_height"</span><span data-if="cpp" style="display:none">"image_height"</span><span data-if="com" style="display:none">"image_height"</span><span data-if="dotnet" style="display:none">"image_height"</span><span data-if="python" style="display:none">"image_height"</span></i> have to
be multiples of 16 pixels, resulting in a minimum of 16 pixels.
</p>
</dd>

<dt><b><i><span data-if="hdevelop" style="display:inline">'pretrained_dl_segmentation_compact.hdl'</span><span data-if="c" style="display:none">"pretrained_dl_segmentation_compact.hdl"</span><span data-if="cpp" style="display:none">"pretrained_dl_segmentation_compact.hdl"</span><span data-if="com" style="display:none">"pretrained_dl_segmentation_compact.hdl"</span><span data-if="dotnet" style="display:none">"pretrained_dl_segmentation_compact.hdl"</span><span data-if="python" style="display:none">"pretrained_dl_segmentation_compact.hdl"</span></i>:</b></dt>
<dd>
<p>

This neural network is designed to handle segmentation tasks with
detailed structures and uses only few memory and is runtime efficient.
</p>
<p>The network architecture allows changes concerning the image dimensions,
but requires a minimum <i><span data-if="hdevelop" style="display:inline">'image_width'</span><span data-if="c" style="display:none">"image_width"</span><span data-if="cpp" style="display:none">"image_width"</span><span data-if="com" style="display:none">"image_width"</span><span data-if="dotnet" style="display:none">"image_width"</span><span data-if="python" style="display:none">"image_width"</span></i> and
<i><span data-if="hdevelop" style="display:inline">'image_height'</span><span data-if="c" style="display:none">"image_height"</span><span data-if="cpp" style="display:none">"image_height"</span><span data-if="com" style="display:none">"image_height"</span><span data-if="dotnet" style="display:none">"image_height"</span><span data-if="python" style="display:none">"image_height"</span></i> of 21 pixels.
</p>
</dd>

<dt><b><i><span data-if="hdevelop" style="display:inline">'pretrained_dl_segmentation_enhanced.hdl'</span><span data-if="c" style="display:none">"pretrained_dl_segmentation_enhanced.hdl"</span><span data-if="cpp" style="display:none">"pretrained_dl_segmentation_enhanced.hdl"</span><span data-if="com" style="display:none">"pretrained_dl_segmentation_enhanced.hdl"</span><span data-if="dotnet" style="display:none">"pretrained_dl_segmentation_enhanced.hdl"</span><span data-if="python" style="display:none">"pretrained_dl_segmentation_enhanced.hdl"</span></i>:</b></dt>
<dd>
<p>

This neural network has more hidden layers than
<i><span data-if="hdevelop" style="display:inline">'pretrained_dl_segmentation_compact.hdl'</span><span data-if="c" style="display:none">"pretrained_dl_segmentation_compact.hdl"</span><span data-if="cpp" style="display:none">"pretrained_dl_segmentation_compact.hdl"</span><span data-if="com" style="display:none">"pretrained_dl_segmentation_compact.hdl"</span><span data-if="dotnet" style="display:none">"pretrained_dl_segmentation_compact.hdl"</span><span data-if="python" style="display:none">"pretrained_dl_segmentation_compact.hdl"</span></i> and is therefore
better suited for segmentation tasks including more complex scenes.
</p>
<p>The network architecture allows changes concerning the image dimensions,
but requires a minimum <i><span data-if="hdevelop" style="display:inline">'image_width'</span><span data-if="c" style="display:none">"image_width"</span><span data-if="cpp" style="display:none">"image_width"</span><span data-if="com" style="display:none">"image_width"</span><span data-if="dotnet" style="display:none">"image_width"</span><span data-if="python" style="display:none">"image_width"</span></i> and
<i><span data-if="hdevelop" style="display:inline">'image_height'</span><span data-if="c" style="display:none">"image_height"</span><span data-if="cpp" style="display:none">"image_height"</span><span data-if="com" style="display:none">"image_height"</span><span data-if="dotnet" style="display:none">"image_height"</span><span data-if="python" style="display:none">"image_height"</span></i> of 47 pixels.
</p>
</dd>
</dl>

</dd>

<dt><b>Models for Deep OCR</b></dt>
<dd>
<p>

The following pretrained neural networks are provided for Deep OCR:
</p>
<dl class="generic">

<dt><b><i><span data-if="hdevelop" style="display:inline">'pretrained_deep_ocr_detection.hdl'</span><span data-if="c" style="display:none">"pretrained_deep_ocr_detection.hdl"</span><span data-if="cpp" style="display:none">"pretrained_deep_ocr_detection.hdl"</span><span data-if="com" style="display:none">"pretrained_deep_ocr_detection.hdl"</span><span data-if="dotnet" style="display:none">"pretrained_deep_ocr_detection.hdl"</span><span data-if="python" style="display:none">"pretrained_deep_ocr_detection.hdl"</span></i>:</b></dt>
<dd>
<p>

This neural network is the default pretrained detection component of a
Deep OCR model, but can be retrained, too. It is designed to detect words
in images.
</p>
<p>This network expects the images to be of the type <code>real</code>.
Additionally, the network is designed for certain image properties. The
corresponding values can be retrieved with <a href="get_dl_model_param.html"><code><span data-if="hdevelop" style="display:inline">get_dl_model_param</span><span data-if="c" style="display:none">get_dl_model_param</span><span data-if="cpp" style="display:none">GetDlModelParam</span><span data-if="com" style="display:none">GetDlModelParam</span><span data-if="dotnet" style="display:none">GetDlModelParam</span><span data-if="python" style="display:none">get_dl_model_param</span></code></a>.
Here we list the default values with which the model has been trained:
</p>
<dl class="generic">

<dt><b></b></dt>
<dd><p>
 <i><span data-if="hdevelop" style="display:inline">'image_width'</span><span data-if="c" style="display:none">"image_width"</span><span data-if="cpp" style="display:none">"image_width"</span><span data-if="com" style="display:none">"image_width"</span><span data-if="dotnet" style="display:none">"image_width"</span><span data-if="python" style="display:none">"image_width"</span></i>: 1024
</p></dd>

<dt><b></b></dt>
<dd><p>
 <i><span data-if="hdevelop" style="display:inline">'image_height'</span><span data-if="c" style="display:none">"image_height"</span><span data-if="cpp" style="display:none">"image_height"</span><span data-if="com" style="display:none">"image_height"</span><span data-if="dotnet" style="display:none">"image_height"</span><span data-if="python" style="display:none">"image_height"</span></i>: 1024
</p></dd>

<dt><b></b></dt>
<dd><p>
 <i><span data-if="hdevelop" style="display:inline">'image_num_channels'</span><span data-if="c" style="display:none">"image_num_channels"</span><span data-if="cpp" style="display:none">"image_num_channels"</span><span data-if="com" style="display:none">"image_num_channels"</span><span data-if="dotnet" style="display:none">"image_num_channels"</span><span data-if="python" style="display:none">"image_num_channels"</span></i>: 3
</p></dd>

<dt><b></b></dt>
<dd><p>
 <i><span data-if="hdevelop" style="display:inline">'image_range_min'</span><span data-if="c" style="display:none">"image_range_min"</span><span data-if="cpp" style="display:none">"image_range_min"</span><span data-if="com" style="display:none">"image_range_min"</span><span data-if="dotnet" style="display:none">"image_range_min"</span><span data-if="python" style="display:none">"image_range_min"</span></i>: -127.0
</p></dd>

<dt><b></b></dt>
<dd><p>
 <i><span data-if="hdevelop" style="display:inline">'image_range_max'</span><span data-if="c" style="display:none">"image_range_max"</span><span data-if="cpp" style="display:none">"image_range_max"</span><span data-if="com" style="display:none">"image_range_max"</span><span data-if="dotnet" style="display:none">"image_range_max"</span><span data-if="python" style="display:none">"image_range_max"</span></i>: 128.0
</p></dd>
</dl>

<p>The network architecture allows changes concerning the image dimensions
<i><span data-if="hdevelop" style="display:inline">'image_width'</span><span data-if="c" style="display:none">"image_width"</span><span data-if="cpp" style="display:none">"image_width"</span><span data-if="com" style="display:none">"image_width"</span><span data-if="dotnet" style="display:none">"image_width"</span><span data-if="python" style="display:none">"image_width"</span></i> and <i><span data-if="hdevelop" style="display:inline">'image_height'</span><span data-if="c" style="display:none">"image_height"</span><span data-if="cpp" style="display:none">"image_height"</span><span data-if="com" style="display:none">"image_height"</span><span data-if="dotnet" style="display:none">"image_height"</span><span data-if="python" style="display:none">"image_height"</span></i>.
</p>
</dd>

<dt><b><i><span data-if="hdevelop" style="display:inline">'pretrained_deep_ocr_detection_compact.hdl'</span><span data-if="c" style="display:none">"pretrained_deep_ocr_detection_compact.hdl"</span><span data-if="cpp" style="display:none">"pretrained_deep_ocr_detection_compact.hdl"</span><span data-if="com" style="display:none">"pretrained_deep_ocr_detection_compact.hdl"</span><span data-if="dotnet" style="display:none">"pretrained_deep_ocr_detection_compact.hdl"</span><span data-if="python" style="display:none">"pretrained_deep_ocr_detection_compact.hdl"</span></i>:</b></dt>
<dd>
<p>

This neural network is a more efficient pretrained network that can be
used as detection component of a Deep OCR model. It is designed to detect
words in images, and it can be retrained as well. This neural network is
designed to be more memory and runtime efficient.
</p>
<p>Regarding the input images and image dimensions, this network has the
same requirements as the default model
<i><span data-if="hdevelop" style="display:inline">'pretrained_deep_ocr_detection_compact.hdl'</span><span data-if="c" style="display:none">"pretrained_deep_ocr_detection_compact.hdl"</span><span data-if="cpp" style="display:none">"pretrained_deep_ocr_detection_compact.hdl"</span><span data-if="com" style="display:none">"pretrained_deep_ocr_detection_compact.hdl"</span><span data-if="dotnet" style="display:none">"pretrained_deep_ocr_detection_compact.hdl"</span><span data-if="python" style="display:none">"pretrained_deep_ocr_detection_compact.hdl"</span></i>.
</p>
</dd>

<dt><b><i><span data-if="hdevelop" style="display:inline">'pretrained_deep_ocr_recognition.hdl'</span><span data-if="c" style="display:none">"pretrained_deep_ocr_recognition.hdl"</span><span data-if="cpp" style="display:none">"pretrained_deep_ocr_recognition.hdl"</span><span data-if="com" style="display:none">"pretrained_deep_ocr_recognition.hdl"</span><span data-if="dotnet" style="display:none">"pretrained_deep_ocr_recognition.hdl"</span><span data-if="python" style="display:none">"pretrained_deep_ocr_recognition.hdl"</span></i>:</b></dt>
<dd>
<p>

This neural network is the default pretrained recognition component of a
Deep OCR model, but can be retrained, too. It is designed to recognize
words in images that are cropped to a single word.
</p>
<p>This network expects the images to be of the type <code>real</code>.
Additionally, the network is designed for certain image properties. The
corresponding values can be retrieved with <a href="get_dl_model_param.html"><code><span data-if="hdevelop" style="display:inline">get_dl_model_param</span><span data-if="c" style="display:none">get_dl_model_param</span><span data-if="cpp" style="display:none">GetDlModelParam</span><span data-if="com" style="display:none">GetDlModelParam</span><span data-if="dotnet" style="display:none">GetDlModelParam</span><span data-if="python" style="display:none">get_dl_model_param</span></code></a>.
Here we list the default values with which the model has been trained:
</p>
<dl class="generic">

<dt><b></b></dt>
<dd><p>
 <i><span data-if="hdevelop" style="display:inline">'image_width'</span><span data-if="c" style="display:none">"image_width"</span><span data-if="cpp" style="display:none">"image_width"</span><span data-if="com" style="display:none">"image_width"</span><span data-if="dotnet" style="display:none">"image_width"</span><span data-if="python" style="display:none">"image_width"</span></i>: 120
</p></dd>

<dt><b></b></dt>
<dd><p>
 <i><span data-if="hdevelop" style="display:inline">'image_height'</span><span data-if="c" style="display:none">"image_height"</span><span data-if="cpp" style="display:none">"image_height"</span><span data-if="com" style="display:none">"image_height"</span><span data-if="dotnet" style="display:none">"image_height"</span><span data-if="python" style="display:none">"image_height"</span></i>: 32
</p></dd>

<dt><b></b></dt>
<dd><p>
 <i><span data-if="hdevelop" style="display:inline">'image_num_channels'</span><span data-if="c" style="display:none">"image_num_channels"</span><span data-if="cpp" style="display:none">"image_num_channels"</span><span data-if="com" style="display:none">"image_num_channels"</span><span data-if="dotnet" style="display:none">"image_num_channels"</span><span data-if="python" style="display:none">"image_num_channels"</span></i>: 1
</p></dd>

<dt><b></b></dt>
<dd><p>
 <i><span data-if="hdevelop" style="display:inline">'image_range_min'</span><span data-if="c" style="display:none">"image_range_min"</span><span data-if="cpp" style="display:none">"image_range_min"</span><span data-if="com" style="display:none">"image_range_min"</span><span data-if="dotnet" style="display:none">"image_range_min"</span><span data-if="python" style="display:none">"image_range_min"</span></i>: -1.0
</p></dd>

<dt><b></b></dt>
<dd><p>
 <i><span data-if="hdevelop" style="display:inline">'image_range_max'</span><span data-if="c" style="display:none">"image_range_max"</span><span data-if="cpp" style="display:none">"image_range_max"</span><span data-if="com" style="display:none">"image_range_max"</span><span data-if="dotnet" style="display:none">"image_range_max"</span><span data-if="python" style="display:none">"image_range_max"</span></i>: 1.0
</p></dd>
</dl>

<p>The network architecture allows changes concerning the image width
<i><span data-if="hdevelop" style="display:inline">'image_width'</span><span data-if="c" style="display:none">"image_width"</span><span data-if="cpp" style="display:none">"image_width"</span><span data-if="com" style="display:none">"image_width"</span><span data-if="dotnet" style="display:none">"image_width"</span><span data-if="python" style="display:none">"image_width"</span></i>. The image height <i><span data-if="hdevelop" style="display:inline">'image_height'</span><span data-if="c" style="display:none">"image_height"</span><span data-if="cpp" style="display:none">"image_height"</span><span data-if="com" style="display:none">"image_height"</span><span data-if="dotnet" style="display:none">"image_height"</span><span data-if="python" style="display:none">"image_height"</span></i> cannot
be changed. The parameter <i><span data-if="hdevelop" style="display:inline">'image_width'</span><span data-if="c" style="display:none">"image_width"</span><span data-if="cpp" style="display:none">"image_width"</span><span data-if="com" style="display:none">"image_width"</span><span data-if="dotnet" style="display:none">"image_width"</span><span data-if="python" style="display:none">"image_width"</span></i> is very important: its
value can be decreased or increased to adapt to the expected lengths of
words, e.g., due to the average width per character. A bigger
<i><span data-if="hdevelop" style="display:inline">'image_width'</span><span data-if="c" style="display:none">"image_width"</span><span data-if="cpp" style="display:none">"image_width"</span><span data-if="com" style="display:none">"image_width"</span><span data-if="dotnet" style="display:none">"image_width"</span><span data-if="python" style="display:none">"image_width"</span></i> will consume more time and memory resources. The
image width <i><span data-if="hdevelop" style="display:inline">'image_width'</span><span data-if="c" style="display:none">"image_width"</span><span data-if="cpp" style="display:none">"image_width"</span><span data-if="com" style="display:none">"image_width"</span><span data-if="dotnet" style="display:none">"image_width"</span><span data-if="python" style="display:none">"image_width"</span></i> may be changed after training.
</p>
</dd>
</dl>

</dd>
</dl>
<h3>Reading in a Model in the ONNX Format</h3>
<p>You can read in an ONNX model, but there are some points to consider.
</p>
<dl class="generic">

<dt><b>Restrictions</b></dt>
<dd>
<p>

Reading in ONNX models with <code><span data-if="hdevelop" style="display:inline">read_dl_model</span><span data-if="c" style="display:none">read_dl_model</span><span data-if="cpp" style="display:none">ReadDlModel</span><span data-if="com" style="display:none">ReadDlModel</span><span data-if="dotnet" style="display:none">ReadDlModel</span><span data-if="python" style="display:none">read_dl_model</span></code>, some restrictions
apply:
</p>
<ul>
<li>
<p> Version 1.8.1 of the ONNX specification is supported. This means only
operators until ONNX operator set version (OpSetVersion) 13 are supported.
For operators with a higher OpSetVersion there is no guarantee that
it can be supported. Further limitations are listed above.
</p>
</li>
<li>
<p> Only 32 bit floating point tensors are supported.
</p>
</li>
<li>
<p> Only models ending with a SoftMax layer are automatically recognized
as classifiers.
All other models are considered as generic model, thus
models of <i><span data-if="hdevelop" style="display:inline">'type'</span><span data-if="c" style="display:none">"type"</span><span data-if="cpp" style="display:none">"type"</span><span data-if="com" style="display:none">"type"</span><span data-if="dotnet" style="display:none">"type"</span><span data-if="python" style="display:none">"type"</span></i> = <i><span data-if="hdevelop" style="display:inline">'generic'</span><span data-if="c" style="display:none">"generic"</span><span data-if="cpp" style="display:none">"generic"</span><span data-if="com" style="display:none">"generic"</span><span data-if="dotnet" style="display:none">"generic"</span><span data-if="python" style="display:none">"generic"</span></i>.
<a href="set_dl_model_param.html"><code><span data-if="hdevelop" style="display:inline">set_dl_model_param</span><span data-if="c" style="display:none">set_dl_model_param</span><span data-if="cpp" style="display:none">SetDlModelParam</span><span data-if="com" style="display:none">SetDlModelParam</span><span data-if="dotnet" style="display:none">SetDlModelParam</span><span data-if="python" style="display:none">set_dl_model_param</span></code></a> can be used to change the model type.
</p>
</li>
<li>
<p> The input graph nodes (images) must be of shape dimension 4:
Number of images (=<i><span data-if="hdevelop" style="display:inline">'batch_size'</span><span data-if="c" style="display:none">"batch_size"</span><span data-if="cpp" style="display:none">"batch_size"</span><span data-if="com" style="display:none">"batch_size"</span><span data-if="dotnet" style="display:none">"batch_size"</span><span data-if="python" style="display:none">"batch_size"</span></i>), <i><span data-if="hdevelop" style="display:inline">'num_channels'</span><span data-if="c" style="display:none">"num_channels"</span><span data-if="cpp" style="display:none">"num_channels"</span><span data-if="com" style="display:none">"num_channels"</span><span data-if="dotnet" style="display:none">"num_channels"</span><span data-if="python" style="display:none">"num_channels"</span></i>,
<i><span data-if="hdevelop" style="display:inline">'image_height'</span><span data-if="c" style="display:none">"image_height"</span><span data-if="cpp" style="display:none">"image_height"</span><span data-if="com" style="display:none">"image_height"</span><span data-if="dotnet" style="display:none">"image_height"</span><span data-if="python" style="display:none">"image_height"</span></i>, and <i><span data-if="hdevelop" style="display:inline">'image_width'</span><span data-if="c" style="display:none">"image_width"</span><span data-if="cpp" style="display:none">"image_width"</span><span data-if="com" style="display:none">"image_width"</span><span data-if="dotnet" style="display:none">"image_width"</span><span data-if="python" style="display:none">"image_width"</span></i>.
</p>
</li>
</ul>

</dd>

<dt><b>Automatic transformations</b></dt>
<dd>
<p>

After reading an ONNX model with <code><span data-if="hdevelop" style="display:inline">read_dl_model</span><span data-if="c" style="display:none">read_dl_model</span><span data-if="cpp" style="display:none">ReadDlModel</span><span data-if="com" style="display:none">ReadDlModel</span><span data-if="dotnet" style="display:none">ReadDlModel</span><span data-if="python" style="display:none">read_dl_model</span></code>, some network
transformations are executed automatically:
</p>
<ul>
<li>
<p> Every non-global pooling layer with a resulting
feature map of size 1x1 is converted to a global pooling layer.
Doing so enables resizable input images.
For more information about pooling layer and possible modes of
operation, see the <code>“Solution Guide on Classification”</code>.
</p>
</li>
<li>
<p> Layer pairs consisting of a convolution layer without activation
and a directly connected activation layer with ReLU activation are
fused.
In order to so do, the output of the convolution layer is only used
as input for the activation layer.
As a result a convolution layer with activation mode ReLU is obtained.
For more information about layers and possible modes of operation,
see the <code>“Solution Guide on Classification”</code>.
</p>
</li>
</ul>

</dd>

<dt><b>Supported operations</b></dt>
<dd>
<p>

ONNX models with the following operations can be read by
<code><span data-if="hdevelop" style="display:inline">read_dl_model</span><span data-if="c" style="display:none">read_dl_model</span><span data-if="cpp" style="display:none">ReadDlModel</span><span data-if="com" style="display:none">ReadDlModel</span><span data-if="dotnet" style="display:none">ReadDlModel</span><span data-if="python" style="display:none">read_dl_model</span></code>:
</p>
<dl class="generic">

<dt><b><code>'Add'</code>:</b></dt>
<dd><p>
 No restrictions.
</p></dd>

<dt><b><code>'ArgMax'</code>:</b></dt>
<dd>
<p>

The following restrictions apply:
</p>
<ul>
<li>
<p> attribute <i><span data-if="hdevelop" style="display:inline">'axis'</span><span data-if="c" style="display:none">"axis"</span><span data-if="cpp" style="display:none">"axis"</span><span data-if="com" style="display:none">"axis"</span><span data-if="dotnet" style="display:none">"axis"</span><span data-if="python" style="display:none">"axis"</span></i>: The value must be <i>1</i>.
</p>
</li>
<li>
<p> attribute <i><span data-if="hdevelop" style="display:inline">'keepdims'</span><span data-if="c" style="display:none">"keepdims"</span><span data-if="cpp" style="display:none">"keepdims"</span><span data-if="com" style="display:none">"keepdims"</span><span data-if="dotnet" style="display:none">"keepdims"</span><span data-if="python" style="display:none">"keepdims"</span></i>: The value must be <i>1</i>.
</p>
</li>
<li>
<p> attribute <i><span data-if="hdevelop" style="display:inline">'select_last_index'</span><span data-if="c" style="display:none">"select_last_index"</span><span data-if="cpp" style="display:none">"select_last_index"</span><span data-if="com" style="display:none">"select_last_index"</span><span data-if="dotnet" style="display:none">"select_last_index"</span><span data-if="python" style="display:none">"select_last_index"</span></i>: The value
must be <i>0</i>.
</p>
</li>
</ul>
</dd>

<dt><b><code>'AveragePool'</code>:</b></dt>
<dd>
<p>

The following restrictions apply:
</p>
<ul>
<li>
<p> attribute <i><span data-if="hdevelop" style="display:inline">'count_include_pad'</span><span data-if="c" style="display:none">"count_include_pad"</span><span data-if="cpp" style="display:none">"count_include_pad"</span><span data-if="com" style="display:none">"count_include_pad"</span><span data-if="dotnet" style="display:none">"count_include_pad"</span><span data-if="python" style="display:none">"count_include_pad"</span></i>: The value must be
<i>0</i>.
</p>
</li>
</ul>
</dd>

<dt><b><code>'BatchNormalization'</code>:</b></dt>
<dd><p>
 No restrictions.
</p></dd>

<dt><b><code>'Clip'</code>:</b></dt>
<dd>
<p>

The following restrictions apply:
</p>
<ul>
<li>
<p> attribute <i><span data-if="hdevelop" style="display:inline">'min'</span><span data-if="c" style="display:none">"min"</span><span data-if="cpp" style="display:none">"min"</span><span data-if="com" style="display:none">"min"</span><span data-if="dotnet" style="display:none">"min"</span><span data-if="python" style="display:none">"min"</span></i>: The value must be <i>0</i>.
</p>
</li>
<li>
<p> attribute <i><span data-if="hdevelop" style="display:inline">'max'</span><span data-if="c" style="display:none">"max"</span><span data-if="cpp" style="display:none">"max"</span><span data-if="com" style="display:none">"max"</span><span data-if="dotnet" style="display:none">"max"</span><span data-if="python" style="display:none">"max"</span></i>: The value must be
greater than <i>0</i> and less than maximum float number.
</p>
</li>
</ul>
</dd>

<dt><b><code>'Concat'</code>:</b></dt>
<dd><p>
 No restrictions.
</p></dd>

<dt><b><code>'Constant'</code>:</b></dt>
<dd>
<p>

The following restrictions apply:
</p>
<ul>
<li>
<p> attribute <i><span data-if="hdevelop" style="display:inline">'sparse_value'</span><span data-if="c" style="display:none">"sparse_value"</span><span data-if="cpp" style="display:none">"sparse_value"</span><span data-if="com" style="display:none">"sparse_value"</span><span data-if="dotnet" style="display:none">"sparse_value"</span><span data-if="python" style="display:none">"sparse_value"</span></i>: The attribute is not supported.
</p>
</li>
<li>
<p> attribute <i><span data-if="hdevelop" style="display:inline">'value'</span><span data-if="c" style="display:none">"value"</span><span data-if="cpp" style="display:none">"value"</span><span data-if="com" style="display:none">"value"</span><span data-if="dotnet" style="display:none">"value"</span><span data-if="python" style="display:none">"value"</span></i>: All entries in the tensor have to be
identical.
</p>
</li>
<li>
<p> attribute <i><span data-if="hdevelop" style="display:inline">'value_floats'</span><span data-if="c" style="display:none">"value_floats"</span><span data-if="cpp" style="display:none">"value_floats"</span><span data-if="com" style="display:none">"value_floats"</span><span data-if="dotnet" style="display:none">"value_floats"</span><span data-if="python" style="display:none">"value_floats"</span></i>: The attribute is not supported.
</p>
</li>
<li>
<p> attribute <i><span data-if="hdevelop" style="display:inline">'value_ints'</span><span data-if="c" style="display:none">"value_ints"</span><span data-if="cpp" style="display:none">"value_ints"</span><span data-if="com" style="display:none">"value_ints"</span><span data-if="dotnet" style="display:none">"value_ints"</span><span data-if="python" style="display:none">"value_ints"</span></i>: The attribute is not supported.
</p>
</li>
<li>
<p> attribute <i><span data-if="hdevelop" style="display:inline">'value_string'</span><span data-if="c" style="display:none">"value_string"</span><span data-if="cpp" style="display:none">"value_string"</span><span data-if="com" style="display:none">"value_string"</span><span data-if="dotnet" style="display:none">"value_string"</span><span data-if="python" style="display:none">"value_string"</span></i>: The attribute is not supported.
</p>
</li>
<li>
<p> attribute <i><span data-if="hdevelop" style="display:inline">'value_strings'</span><span data-if="c" style="display:none">"value_strings"</span><span data-if="cpp" style="display:none">"value_strings"</span><span data-if="com" style="display:none">"value_strings"</span><span data-if="dotnet" style="display:none">"value_strings"</span><span data-if="python" style="display:none">"value_strings"</span></i>: The attribute is not supported.
</p>
</li>
</ul>
</dd>

<dt><b><code>'Conv'</code>:</b></dt>
<dd>
<p>

The following restrictions apply:
</p>
<ul>
<li>
<p> attribute <i><span data-if="hdevelop" style="display:inline">'pads'</span><span data-if="c" style="display:none">"pads"</span><span data-if="cpp" style="display:none">"pads"</span><span data-if="com" style="display:none">"pads"</span><span data-if="dotnet" style="display:none">"pads"</span><span data-if="python" style="display:none">"pads"</span></i>: Padding values greater than
or equal to kernel size are not supported.
</p>
</li>
</ul>
</dd>

<dt><b><code>'ConvTranspose'</code>:</b></dt>
<dd>
<p>

The following restrictions apply:
</p>
<ul>
<li>
<p> attribute <i><span data-if="hdevelop" style="display:inline">'dilations'</span><span data-if="c" style="display:none">"dilations"</span><span data-if="cpp" style="display:none">"dilations"</span><span data-if="com" style="display:none">"dilations"</span><span data-if="dotnet" style="display:none">"dilations"</span><span data-if="python" style="display:none">"dilations"</span></i>: Only the value <i><span data-if="hdevelop" style="display:inline">'(1, 1)'</span><span data-if="c" style="display:none">"(1, 1)"</span><span data-if="cpp" style="display:none">"(1, 1)"</span><span data-if="com" style="display:none">"(1, 1)"</span><span data-if="dotnet" style="display:none">"(1, 1)"</span><span data-if="python" style="display:none">"(1, 1)"</span></i>
(no dilations) is supported.
</p>
</li>
<li>
<p> attribute <i><span data-if="hdevelop" style="display:inline">'group'</span><span data-if="c" style="display:none">"group"</span><span data-if="cpp" style="display:none">"group"</span><span data-if="com" style="display:none">"group"</span><span data-if="dotnet" style="display:none">"group"</span><span data-if="python" style="display:none">"group"</span></i>: Only the value <i>1</i> is supported
(no grouped transposed convolution).
</p>
</li>
<li>
<p> attribute <i><span data-if="hdevelop" style="display:inline">'kernel_shape'</span><span data-if="c" style="display:none">"kernel_shape"</span><span data-if="cpp" style="display:none">"kernel_shape"</span><span data-if="com" style="display:none">"kernel_shape"</span><span data-if="dotnet" style="display:none">"kernel_shape"</span><span data-if="python" style="display:none">"kernel_shape"</span></i>: Only symmetric kernel shapes
are supported.
</p>
</li>
<li>
<p> attribute <i><span data-if="hdevelop" style="display:inline">'output_padding'</span><span data-if="c" style="display:none">"output_padding"</span><span data-if="cpp" style="display:none">"output_padding"</span><span data-if="com" style="display:none">"output_padding"</span><span data-if="dotnet" style="display:none">"output_padding"</span><span data-if="python" style="display:none">"output_padding"</span></i>: See restrictions mentioned in
<a href="create_dl_layer_transposed_convolution.html"><code><span data-if="hdevelop" style="display:inline">create_dl_layer_transposed_convolution</span><span data-if="c" style="display:none">create_dl_layer_transposed_convolution</span><span data-if="cpp" style="display:none">CreateDlLayerTransposedConvolution</span><span data-if="com" style="display:none">CreateDlLayerTransposedConvolution</span><span data-if="dotnet" style="display:none">CreateDlLayerTransposedConvolution</span><span data-if="python" style="display:none">create_dl_layer_transposed_convolution</span></code></a>.
</p>
</li>
<li>
<p> attribute <i><span data-if="hdevelop" style="display:inline">'output_shape'</span><span data-if="c" style="display:none">"output_shape"</span><span data-if="cpp" style="display:none">"output_shape"</span><span data-if="com" style="display:none">"output_shape"</span><span data-if="dotnet" style="display:none">"output_shape"</span><span data-if="python" style="display:none">"output_shape"</span></i>: The attribute is not supported.
</p>
</li>
<li>
<p> attribute <i><span data-if="hdevelop" style="display:inline">'pads'</span><span data-if="c" style="display:none">"pads"</span><span data-if="cpp" style="display:none">"pads"</span><span data-if="com" style="display:none">"pads"</span><span data-if="dotnet" style="display:none">"pads"</span><span data-if="python" style="display:none">"pads"</span></i>: Padding values greater than
or equal to kernel size are not supported.
</p>
</li>
<li>
<p> attribute <i><span data-if="hdevelop" style="display:inline">'strides'</span><span data-if="c" style="display:none">"strides"</span><span data-if="cpp" style="display:none">"strides"</span><span data-if="com" style="display:none">"strides"</span><span data-if="dotnet" style="display:none">"strides"</span><span data-if="python" style="display:none">"strides"</span></i>: Only symmetric strides are supported.
</p>
</li>
</ul>
</dd>

<dt><b><code>'DepthToSpace'</code>:</b></dt>
<dd>
<p>

The following restrictions apply:
</p>
<ul>
<li>
<p> attribute <i><span data-if="hdevelop" style="display:inline">'mode'</span><span data-if="c" style="display:none">"mode"</span><span data-if="cpp" style="display:none">"mode"</span><span data-if="com" style="display:none">"mode"</span><span data-if="dotnet" style="display:none">"mode"</span><span data-if="python" style="display:none">"mode"</span></i>: The value must be <i><span data-if="hdevelop" style="display:inline">'CRD'</span><span data-if="c" style="display:none">"CRD"</span><span data-if="cpp" style="display:none">"CRD"</span><span data-if="com" style="display:none">"CRD"</span><span data-if="dotnet" style="display:none">"CRD"</span><span data-if="python" style="display:none">"CRD"</span></i>.
</p>
</li>
</ul>
</dd>

<dt><b><code>'Dropout'</code>:</b></dt>
<dd><p>
 No restrictions.
</p></dd>

<dt><b><code>'Gemm'</code>:</b></dt>
<dd>
<p>

The following restrictions apply:
</p>
<ul>
<li>
<p> attribute <i><span data-if="hdevelop" style="display:inline">'alpha'</span><span data-if="c" style="display:none">"alpha"</span><span data-if="cpp" style="display:none">"alpha"</span><span data-if="com" style="display:none">"alpha"</span><span data-if="dotnet" style="display:none">"alpha"</span><span data-if="python" style="display:none">"alpha"</span></i>: The value must be <i>1</i>.
</p>
</li>
<li>
<p> attribute <i><span data-if="hdevelop" style="display:inline">'beta'</span><span data-if="c" style="display:none">"beta"</span><span data-if="cpp" style="display:none">"beta"</span><span data-if="com" style="display:none">"beta"</span><span data-if="dotnet" style="display:none">"beta"</span><span data-if="python" style="display:none">"beta"</span></i>: The value must be <i>1</i>.
</p>
</li>
<li>
<p> attribute <i><span data-if="hdevelop" style="display:inline">'transA'</span><span data-if="c" style="display:none">"transA"</span><span data-if="cpp" style="display:none">"transA"</span><span data-if="com" style="display:none">"transA"</span><span data-if="dotnet" style="display:none">"transA"</span><span data-if="python" style="display:none">"transA"</span></i>: The value must be
<i>0</i>.
</p>
</li>
</ul>
</dd>

<dt><b><code>'GlobalAveragePool'</code>:</b></dt>
<dd><p>
 No restrictions.
</p></dd>

<dt><b><code>'GlobalMaxPool'</code>:</b></dt>
<dd>
<p>

The following restrictions apply:
</p>
<ul>
<li>
<p> attribute <i><span data-if="hdevelop" style="display:inline">'dilations'</span><span data-if="c" style="display:none">"dilations"</span><span data-if="cpp" style="display:none">"dilations"</span><span data-if="com" style="display:none">"dilations"</span><span data-if="dotnet" style="display:none">"dilations"</span><span data-if="python" style="display:none">"dilations"</span></i>: The value must be <i>1</i>.
</p>
</li>
</ul>
</dd>

<dt><b><code>'LeakyRelu'</code>:</b></dt>
<dd><p>
 No restrictions.
</p></dd>

<dt><b><code>'LogSoftmax'</code>:</b></dt>
<dd>
<p>

The following restrictions apply:
</p>
<ul>
<li>
<p> attribute <i><span data-if="hdevelop" style="display:inline">'axis'</span><span data-if="c" style="display:none">"axis"</span><span data-if="cpp" style="display:none">"axis"</span><span data-if="com" style="display:none">"axis"</span><span data-if="dotnet" style="display:none">"axis"</span><span data-if="python" style="display:none">"axis"</span></i>: The value must be <i>1</i>.
</p>
</li>
</ul>
</dd>

<dt><b><code>'LRN'</code>:</b></dt>
<dd><p>
 No restrictions. Hint: Attribute <i><span data-if="hdevelop" style="display:inline">'size'</span><span data-if="c" style="display:none">"size"</span><span data-if="cpp" style="display:none">"size"</span><span data-if="com" style="display:none">"size"</span><span data-if="dotnet" style="display:none">"size"</span><span data-if="python" style="display:none">"size"</span></i>
has no effect.
</p></dd>

<dt><b><code>'MaxPool'</code>:</b></dt>
<dd><p>
 No restrictions.
</p></dd>

<dt><b><code>'Mean'</code>:</b></dt>
<dd><p>
 No restrictions.
</p></dd>

<dt><b><code>'Mul'</code>:</b></dt>
<dd><p>
 No restrictions.
</p></dd>

<dt><b><code>'ReduceMax'</code>:</b></dt>
<dd>
<p>

The following restrictions apply:
</p>
<ul>
<li>
<p> attribute <i><span data-if="hdevelop" style="display:inline">'axes'</span><span data-if="c" style="display:none">"axes"</span><span data-if="cpp" style="display:none">"axes"</span><span data-if="com" style="display:none">"axes"</span><span data-if="dotnet" style="display:none">"axes"</span><span data-if="python" style="display:none">"axes"</span></i>: The value must be <i>1</i>.
</p>
</li>
<li>
<p> attribute <i><span data-if="hdevelop" style="display:inline">'keepdims'</span><span data-if="c" style="display:none">"keepdims"</span><span data-if="cpp" style="display:none">"keepdims"</span><span data-if="com" style="display:none">"keepdims"</span><span data-if="dotnet" style="display:none">"keepdims"</span><span data-if="python" style="display:none">"keepdims"</span></i>: The value must be <i>1</i>.
</p>
</li>
</ul>
</dd>

<dt><b><code>'Relu'</code>:</b></dt>
<dd><p>
 No restrictions.
</p></dd>

<dt><b><code>'Resize'</code>:</b></dt>
<dd>
<p>

The following restrictions apply:
</p>
<ul>
<li>
<p> attribute <i><span data-if="hdevelop" style="display:inline">'mode'</span><span data-if="c" style="display:none">"mode"</span><span data-if="cpp" style="display:none">"mode"</span><span data-if="com" style="display:none">"mode"</span><span data-if="dotnet" style="display:none">"mode"</span><span data-if="python" style="display:none">"mode"</span></i>: Only the values <i><span data-if="hdevelop" style="display:inline">'linear'</span><span data-if="c" style="display:none">"linear"</span><span data-if="cpp" style="display:none">"linear"</span><span data-if="com" style="display:none">"linear"</span><span data-if="dotnet" style="display:none">"linear"</span><span data-if="python" style="display:none">"linear"</span></i> or
<i><span data-if="hdevelop" style="display:inline">'bilinear'</span><span data-if="c" style="display:none">"bilinear"</span><span data-if="cpp" style="display:none">"bilinear"</span><span data-if="com" style="display:none">"bilinear"</span><span data-if="dotnet" style="display:none">"bilinear"</span><span data-if="python" style="display:none">"bilinear"</span></i> are supported.
</p>
</li>
<li>
<p> attribute <i><span data-if="hdevelop" style="display:inline">'coordinate_transformation_mode'</span><span data-if="c" style="display:none">"coordinate_transformation_mode"</span><span data-if="cpp" style="display:none">"coordinate_transformation_mode"</span><span data-if="com" style="display:none">"coordinate_transformation_mode"</span><span data-if="dotnet" style="display:none">"coordinate_transformation_mode"</span><span data-if="python" style="display:none">"coordinate_transformation_mode"</span></i>: Only the values
<i><span data-if="hdevelop" style="display:inline">'pytorch_half_pixel'</span><span data-if="c" style="display:none">"pytorch_half_pixel"</span><span data-if="cpp" style="display:none">"pytorch_half_pixel"</span><span data-if="com" style="display:none">"pytorch_half_pixel"</span><span data-if="dotnet" style="display:none">"pytorch_half_pixel"</span><span data-if="python" style="display:none">"pytorch_half_pixel"</span></i> and <i><span data-if="hdevelop" style="display:inline">'align_corners'</span><span data-if="c" style="display:none">"align_corners"</span><span data-if="cpp" style="display:none">"align_corners"</span><span data-if="com" style="display:none">"align_corners"</span><span data-if="dotnet" style="display:none">"align_corners"</span><span data-if="python" style="display:none">"align_corners"</span></i> are
supported.
</p>
</li>
<li>
<p> input tensor <i><span data-if="hdevelop" style="display:inline">'roi'</span><span data-if="c" style="display:none">"roi"</span><span data-if="cpp" style="display:none">"roi"</span><span data-if="com" style="display:none">"roi"</span><span data-if="dotnet" style="display:none">"roi"</span><span data-if="python" style="display:none">"roi"</span></i>: If values are set they have no
effect on the inference.
</p>
</li>
<li>
<p> The attributes <i><span data-if="hdevelop" style="display:inline">'cubic_coeff_a'</span><span data-if="c" style="display:none">"cubic_coeff_a"</span><span data-if="cpp" style="display:none">"cubic_coeff_a"</span><span data-if="com" style="display:none">"cubic_coeff_a"</span><span data-if="dotnet" style="display:none">"cubic_coeff_a"</span><span data-if="python" style="display:none">"cubic_coeff_a"</span></i>, <i><span data-if="hdevelop" style="display:inline">'exclude_outside'</span><span data-if="c" style="display:none">"exclude_outside"</span><span data-if="cpp" style="display:none">"exclude_outside"</span><span data-if="com" style="display:none">"exclude_outside"</span><span data-if="dotnet" style="display:none">"exclude_outside"</span><span data-if="python" style="display:none">"exclude_outside"</span></i>,
<i><span data-if="hdevelop" style="display:inline">'extrapolation_value'</span><span data-if="c" style="display:none">"extrapolation_value"</span><span data-if="cpp" style="display:none">"extrapolation_value"</span><span data-if="com" style="display:none">"extrapolation_value"</span><span data-if="dotnet" style="display:none">"extrapolation_value"</span><span data-if="python" style="display:none">"extrapolation_value"</span></i>, or <i><span data-if="hdevelop" style="display:inline">'nearest_mode'</span><span data-if="c" style="display:none">"nearest_mode"</span><span data-if="cpp" style="display:none">"nearest_mode"</span><span data-if="com" style="display:none">"nearest_mode"</span><span data-if="dotnet" style="display:none">"nearest_mode"</span><span data-if="python" style="display:none">"nearest_mode"</span></i> have no
effect.
</p>
</li>
</ul>
</dd>

<dt><b><code>'Reshape'</code>:</b></dt>
<dd>
<p>

The following restrictions apply:
</p>
<ul>
<li>
<p> attribute <i><span data-if="hdevelop" style="display:inline">'allowzero'</span><span data-if="c" style="display:none">"allowzero"</span><span data-if="cpp" style="display:none">"allowzero"</span><span data-if="com" style="display:none">"allowzero"</span><span data-if="dotnet" style="display:none">"allowzero"</span><span data-if="python" style="display:none">"allowzero"</span></i>: If the attribute is used its value
must be <i>0</i>.
</p>
</li>
</ul>
</dd>

<dt><b><code>'Sigmoid'</code>:</b></dt>
<dd><p>
 No restrictions.
</p></dd>

<dt><b><code>'Softmax'</code>:</b></dt>
<dd>
<p>

The following restrictions apply:
</p>
<ul>
<li>
<p> attribute <i><span data-if="hdevelop" style="display:inline">'axis'</span><span data-if="c" style="display:none">"axis"</span><span data-if="cpp" style="display:none">"axis"</span><span data-if="com" style="display:none">"axis"</span><span data-if="dotnet" style="display:none">"axis"</span><span data-if="python" style="display:none">"axis"</span></i>: If the attribute is used its value must
be <i>1</i>.
</p>
</li>
</ul>
</dd>

<dt><b><code>'Sub'</code>:</b></dt>
<dd><p>
 No restrictions.
</p></dd>

<dt><b><code>'Sum'</code>:</b></dt>
<dd><p>
 No restrictions.
</p></dd>

<dt><b><code>'Transpose'</code>:</b></dt>
<dd><p>
 No restrictions.
</p></dd>
</dl>
<p>
Moreover the ONNX <code>'metadata_props'</code> field is supported. It is written to
the model parameter <i><span data-if="hdevelop" style="display:inline">'meta_data'</span><span data-if="c" style="display:none">"meta_data"</span><span data-if="cpp" style="display:none">"meta_data"</span><span data-if="com" style="display:none">"meta_data"</span><span data-if="dotnet" style="display:none">"meta_data"</span><span data-if="python" style="display:none">"meta_data"</span></i>.
</p>
</dd>
</dl>
<h2 id="sec_execution">运行信息</h2>
<ul>
  <li>多线程类型:可重入(与非独占操作符并行运行)。</li>
<li>多线程作用域:全局(可以从任何线程调用)。</li>
  <li>未经并行化处理。</li>
</ul>
<p>This operator returns a handle. Note that the state of an instance of this handle type may be changed by specific operators even though the handle is used as an input parameter by those operators.</p>
<h2 id="sec_parameters">参数表</h2>
  <div class="par">
<div class="parhead">
<span id="FileName" class="parname"><b><code><span data-if="hdevelop" style="display:inline">FileName</span><span data-if="c" style="display:none">FileName</span><span data-if="cpp" style="display:none">FileName</span><span data-if="com" style="display:none">FileName</span><span data-if="dotnet" style="display:none">fileName</span><span data-if="python" style="display:none">file_name</span></code></b> (input_control)  </span><span>filename.read <code>→</code> <span data-if="dotnet" style="display:none"><a href="HTuple.html">HTuple</a></span><span data-if="python" style="display:none">str</span><span data-if="cpp" style="display:none"><a href="HTuple.html">HTuple</a></span><span data-if="c" style="display:none">Htuple</span><span data-if="hdevelop" style="display:inline"> (string)</span><span data-if="dotnet" style="display:none"> (<i>string</i>)</span><span data-if="cpp" style="display:none"> (<i>HString</i>)</span><span data-if="c" style="display:none"> (<i>char*</i>)</span></span>
</div>
<p class="pardesc">Filename</p>
<p class="pardesc"><span class="parcat">Default:
      </span>
    <span data-if="hdevelop" style="display:inline">'pretrained_dl_classifier_compact.hdl'</span>
    <span data-if="c" style="display:none">"pretrained_dl_classifier_compact.hdl"</span>
    <span data-if="cpp" style="display:none">"pretrained_dl_classifier_compact.hdl"</span>
    <span data-if="com" style="display:none">"pretrained_dl_classifier_compact.hdl"</span>
    <span data-if="dotnet" style="display:none">"pretrained_dl_classifier_compact.hdl"</span>
    <span data-if="python" style="display:none">"pretrained_dl_classifier_compact.hdl"</span>
</p>
<p class="pardesc"><span class="parcat">List of values:
      </span><span data-if="hdevelop" style="display:inline">'initial_dl_anomaly_large.hdl'</span><span data-if="c" style="display:none">"initial_dl_anomaly_large.hdl"</span><span data-if="cpp" style="display:none">"initial_dl_anomaly_large.hdl"</span><span data-if="com" style="display:none">"initial_dl_anomaly_large.hdl"</span><span data-if="dotnet" style="display:none">"initial_dl_anomaly_large.hdl"</span><span data-if="python" style="display:none">"initial_dl_anomaly_large.hdl"</span>, <span data-if="hdevelop" style="display:inline">'initial_dl_anomaly_medium.hdl'</span><span data-if="c" style="display:none">"initial_dl_anomaly_medium.hdl"</span><span data-if="cpp" style="display:none">"initial_dl_anomaly_medium.hdl"</span><span data-if="com" style="display:none">"initial_dl_anomaly_medium.hdl"</span><span data-if="dotnet" style="display:none">"initial_dl_anomaly_medium.hdl"</span><span data-if="python" style="display:none">"initial_dl_anomaly_medium.hdl"</span>, <span data-if="hdevelop" style="display:inline">'pretrained_deep_ocr_detection.hdl'</span><span data-if="c" style="display:none">"pretrained_deep_ocr_detection.hdl"</span><span data-if="cpp" style="display:none">"pretrained_deep_ocr_detection.hdl"</span><span data-if="com" style="display:none">"pretrained_deep_ocr_detection.hdl"</span><span data-if="dotnet" style="display:none">"pretrained_deep_ocr_detection.hdl"</span><span data-if="python" style="display:none">"pretrained_deep_ocr_detection.hdl"</span>, <span data-if="hdevelop" style="display:inline">'pretrained_deep_ocr_detection_compact.hdl'</span><span data-if="c" style="display:none">"pretrained_deep_ocr_detection_compact.hdl"</span><span data-if="cpp" style="display:none">"pretrained_deep_ocr_detection_compact.hdl"</span><span data-if="com" style="display:none">"pretrained_deep_ocr_detection_compact.hdl"</span><span data-if="dotnet" style="display:none">"pretrained_deep_ocr_detection_compact.hdl"</span><span data-if="python" style="display:none">"pretrained_deep_ocr_detection_compact.hdl"</span>, <span data-if="hdevelop" style="display:inline">'pretrained_deep_ocr_recognition.hdl'</span><span data-if="c" style="display:none">"pretrained_deep_ocr_recognition.hdl"</span><span data-if="cpp" style="display:none">"pretrained_deep_ocr_recognition.hdl"</span><span data-if="com" style="display:none">"pretrained_deep_ocr_recognition.hdl"</span><span data-if="dotnet" style="display:none">"pretrained_deep_ocr_recognition.hdl"</span><span data-if="python" style="display:none">"pretrained_deep_ocr_recognition.hdl"</span>, <span data-if="hdevelop" style="display:inline">'pretrained_dl_3d_gripping_point.hdl'</span><span data-if="c" style="display:none">"pretrained_dl_3d_gripping_point.hdl"</span><span data-if="cpp" style="display:none">"pretrained_dl_3d_gripping_point.hdl"</span><span data-if="com" style="display:none">"pretrained_dl_3d_gripping_point.hdl"</span><span data-if="dotnet" style="display:none">"pretrained_dl_3d_gripping_point.hdl"</span><span data-if="python" style="display:none">"pretrained_dl_3d_gripping_point.hdl"</span>, <span data-if="hdevelop" style="display:inline">'pretrained_dl_anomaly_global_context.hdl'</span><span data-if="c" style="display:none">"pretrained_dl_anomaly_global_context.hdl"</span><span data-if="cpp" style="display:none">"pretrained_dl_anomaly_global_context.hdl"</span><span data-if="com" style="display:none">"pretrained_dl_anomaly_global_context.hdl"</span><span data-if="dotnet" style="display:none">"pretrained_dl_anomaly_global_context.hdl"</span><span data-if="python" style="display:none">"pretrained_dl_anomaly_global_context.hdl"</span>, <span data-if="hdevelop" style="display:inline">'pretrained_dl_classifier_alexnet.hdl'</span><span data-if="c" style="display:none">"pretrained_dl_classifier_alexnet.hdl"</span><span data-if="cpp" style="display:none">"pretrained_dl_classifier_alexnet.hdl"</span><span data-if="com" style="display:none">"pretrained_dl_classifier_alexnet.hdl"</span><span data-if="dotnet" style="display:none">"pretrained_dl_classifier_alexnet.hdl"</span><span data-if="python" style="display:none">"pretrained_dl_classifier_alexnet.hdl"</span>, <span data-if="hdevelop" style="display:inline">'pretrained_dl_classifier_compact.hdl'</span><span data-if="c" style="display:none">"pretrained_dl_classifier_compact.hdl"</span><span data-if="cpp" style="display:none">"pretrained_dl_classifier_compact.hdl"</span><span data-if="com" style="display:none">"pretrained_dl_classifier_compact.hdl"</span><span data-if="dotnet" style="display:none">"pretrained_dl_classifier_compact.hdl"</span><span data-if="python" style="display:none">"pretrained_dl_classifier_compact.hdl"</span>, <span data-if="hdevelop" style="display:inline">'pretrained_dl_classifier_enhanced.hdl'</span><span data-if="c" style="display:none">"pretrained_dl_classifier_enhanced.hdl"</span><span data-if="cpp" style="display:none">"pretrained_dl_classifier_enhanced.hdl"</span><span data-if="com" style="display:none">"pretrained_dl_classifier_enhanced.hdl"</span><span data-if="dotnet" style="display:none">"pretrained_dl_classifier_enhanced.hdl"</span><span data-if="python" style="display:none">"pretrained_dl_classifier_enhanced.hdl"</span>, <span data-if="hdevelop" style="display:inline">'pretrained_dl_classifier_mobilenet_v2.hdl'</span><span data-if="c" style="display:none">"pretrained_dl_classifier_mobilenet_v2.hdl"</span><span data-if="cpp" style="display:none">"pretrained_dl_classifier_mobilenet_v2.hdl"</span><span data-if="com" style="display:none">"pretrained_dl_classifier_mobilenet_v2.hdl"</span><span data-if="dotnet" style="display:none">"pretrained_dl_classifier_mobilenet_v2.hdl"</span><span data-if="python" style="display:none">"pretrained_dl_classifier_mobilenet_v2.hdl"</span>, <span data-if="hdevelop" style="display:inline">'pretrained_dl_classifier_resnet18.hdl'</span><span data-if="c" style="display:none">"pretrained_dl_classifier_resnet18.hdl"</span><span data-if="cpp" style="display:none">"pretrained_dl_classifier_resnet18.hdl"</span><span data-if="com" style="display:none">"pretrained_dl_classifier_resnet18.hdl"</span><span data-if="dotnet" style="display:none">"pretrained_dl_classifier_resnet18.hdl"</span><span data-if="python" style="display:none">"pretrained_dl_classifier_resnet18.hdl"</span>, <span data-if="hdevelop" style="display:inline">'pretrained_dl_classifier_resnet50.hdl'</span><span data-if="c" style="display:none">"pretrained_dl_classifier_resnet50.hdl"</span><span data-if="cpp" style="display:none">"pretrained_dl_classifier_resnet50.hdl"</span><span data-if="com" style="display:none">"pretrained_dl_classifier_resnet50.hdl"</span><span data-if="dotnet" style="display:none">"pretrained_dl_classifier_resnet50.hdl"</span><span data-if="python" style="display:none">"pretrained_dl_classifier_resnet50.hdl"</span>, <span data-if="hdevelop" style="display:inline">'pretrained_dl_edge_extractor.hdl'</span><span data-if="c" style="display:none">"pretrained_dl_edge_extractor.hdl"</span><span data-if="cpp" style="display:none">"pretrained_dl_edge_extractor.hdl"</span><span data-if="com" style="display:none">"pretrained_dl_edge_extractor.hdl"</span><span data-if="dotnet" style="display:none">"pretrained_dl_edge_extractor.hdl"</span><span data-if="python" style="display:none">"pretrained_dl_edge_extractor.hdl"</span>, <span data-if="hdevelop" style="display:inline">'pretrained_dl_segmentation_compact.hdl'</span><span data-if="c" style="display:none">"pretrained_dl_segmentation_compact.hdl"</span><span data-if="cpp" style="display:none">"pretrained_dl_segmentation_compact.hdl"</span><span data-if="com" style="display:none">"pretrained_dl_segmentation_compact.hdl"</span><span data-if="dotnet" style="display:none">"pretrained_dl_segmentation_compact.hdl"</span><span data-if="python" style="display:none">"pretrained_dl_segmentation_compact.hdl"</span>, <span data-if="hdevelop" style="display:inline">'pretrained_dl_segmentation_enhanced.hdl'</span><span data-if="c" style="display:none">"pretrained_dl_segmentation_enhanced.hdl"</span><span data-if="cpp" style="display:none">"pretrained_dl_segmentation_enhanced.hdl"</span><span data-if="com" style="display:none">"pretrained_dl_segmentation_enhanced.hdl"</span><span data-if="dotnet" style="display:none">"pretrained_dl_segmentation_enhanced.hdl"</span><span data-if="python" style="display:none">"pretrained_dl_segmentation_enhanced.hdl"</span></p>
<p class="pardesc"><span class="parcat">File extension:
          </span>.<code>hdl</code>, .<code>onnx</code></p>
</div>
  <div class="par">
<div class="parhead">
<span id="DLModelHandle" class="parname"><b><code><span data-if="hdevelop" style="display:inline">DLModelHandle</span><span data-if="c" style="display:none">DLModelHandle</span><span data-if="cpp" style="display:none">DLModelHandle</span><span data-if="com" style="display:none">DLModelHandle</span><span data-if="dotnet" style="display:none">DLModelHandle</span><span data-if="python" style="display:none">dlmodel_handle</span></code></b> (output_control)  </span><span>dl_model <code>→</code> <span data-if="dotnet" style="display:none"><a href="HDlModel.html">HDlModel</a>, </span><span data-if="dotnet" style="display:none"><a href="HTuple.html">HTuple</a></span><span data-if="python" style="display:none">HHandle</span><span data-if="cpp" style="display:none"><a href="HTuple.html">HTuple</a></span><span data-if="c" style="display:none">Htuple</span><span data-if="hdevelop" style="display:inline"> (handle)</span><span data-if="dotnet" style="display:none"> (<i>IntPtr</i>)</span><span data-if="cpp" style="display:none"> (<i>HHandle</i>)</span><span data-if="c" style="display:none"> (<i>handle</i>)</span></span>
</div>
<p class="pardesc">Handle of the deep learning model.</p>
</div>
<h2 id="sec_result">结果</h2>
<p>如果参数均有效，算子 <code><span data-if="hdevelop" style="display:inline">read_dl_model</span><span data-if="c" style="display:none">read_dl_model</span><span data-if="cpp" style="display:none">ReadDlModel</span><span data-if="com" style="display:none">ReadDlModel</span><span data-if="dotnet" style="display:none">ReadDlModel</span><span data-if="python" style="display:none">read_dl_model</span></code>
返回值 <TT>2</TT> (
      <TT>H_MSG_TRUE</TT>)
    . 如有必要，将引发异常。</p>
<h2 id="sec_successors">可能的后置算子</h2>
<p>
<code><a href="set_dl_model_param.html"><span data-if="hdevelop" style="display:inline">set_dl_model_param</span><span data-if="c" style="display:none">set_dl_model_param</span><span data-if="cpp" style="display:none">SetDlModelParam</span><span data-if="com" style="display:none">SetDlModelParam</span><span data-if="dotnet" style="display:none">SetDlModelParam</span><span data-if="python" style="display:none">set_dl_model_param</span></a></code>, 
<code><a href="get_dl_model_param.html"><span data-if="hdevelop" style="display:inline">get_dl_model_param</span><span data-if="c" style="display:none">get_dl_model_param</span><span data-if="cpp" style="display:none">GetDlModelParam</span><span data-if="com" style="display:none">GetDlModelParam</span><span data-if="dotnet" style="display:none">GetDlModelParam</span><span data-if="python" style="display:none">get_dl_model_param</span></a></code>, 
<code><a href="apply_dl_model.html"><span data-if="hdevelop" style="display:inline">apply_dl_model</span><span data-if="c" style="display:none">apply_dl_model</span><span data-if="cpp" style="display:none">ApplyDlModel</span><span data-if="com" style="display:none">ApplyDlModel</span><span data-if="dotnet" style="display:none">ApplyDlModel</span><span data-if="python" style="display:none">apply_dl_model</span></a></code>, 
<code><a href="train_dl_model_batch.html"><span data-if="hdevelop" style="display:inline">train_dl_model_batch</span><span data-if="c" style="display:none">train_dl_model_batch</span><span data-if="cpp" style="display:none">TrainDlModelBatch</span><span data-if="com" style="display:none">TrainDlModelBatch</span><span data-if="dotnet" style="display:none">TrainDlModelBatch</span><span data-if="python" style="display:none">train_dl_model_batch</span></a></code>, 
<code><a href="train_dl_model_anomaly_dataset.html"><span data-if="hdevelop" style="display:inline">train_dl_model_anomaly_dataset</span><span data-if="c" style="display:none">train_dl_model_anomaly_dataset</span><span data-if="cpp" style="display:none">TrainDlModelAnomalyDataset</span><span data-if="com" style="display:none">TrainDlModelAnomalyDataset</span><span data-if="dotnet" style="display:none">TrainDlModelAnomalyDataset</span><span data-if="python" style="display:none">train_dl_model_anomaly_dataset</span></a></code>
</p>
<h2 id="sec_alternatives">可替代算子</h2>
<p>
<code><a href="create_dl_model_detection.html"><span data-if="hdevelop" style="display:inline">create_dl_model_detection</span><span data-if="c" style="display:none">create_dl_model_detection</span><span data-if="cpp" style="display:none">CreateDlModelDetection</span><span data-if="com" style="display:none">CreateDlModelDetection</span><span data-if="dotnet" style="display:none">CreateDlModelDetection</span><span data-if="python" style="display:none">create_dl_model_detection</span></a></code>
</p>
<h2 id="sec_references">References</h2>
<p>

Open Neural Network Exchange (ONNX), https://onnx.ai/
</p>
<h2 id="sec_module">模块</h2>
<p>
Foundation. This operator uses dynamic licensing (see the ``Installation Guide''). Which of the following modules is required depends on the specific usage of 该算子:<br>3D Metrology, OCR/OCV, Matching, Deep Learning Inference</p>
<!--OP_REF_FOOTER_START-->
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